Master of Information Technology
Course code: MC-IT
2 years full time / 4 years part time
1 or 1.5 years full time (or part time equivalent) with relevant prior qualifications
March, July
Commonwealth Supported Places (CSPs) available
Access Melbourne is available
March, July
AUD $64,000 (2026 indicative first year fee)
IELTS 6.5: with no band less than 6.0
Course structure
Overview
The Master of Information Technology is a 1–2 years degree (full-time), depending on your prior work experience and study.
Core subjects
If you’re new to information technology, you’ll undertake four core subjects in Programming and Software Development, Algorithms and Complexity, Internet Technologies and Database Systems & Information Modelling .
If you have previously undertaken study in IT or worked in the field, you may be eligible for credit, enabling you to advance into our specialised subjects.
Optional specialisations
In the Master of Information Technology students may choose not to specialise and use elective subjects to develop skills for a career in app development, data analytics, game development and more. Or you may choose one of the following optional specialisations:
Artificial Intelligence
Develop expertise in the design, implementation and analysis of systems that learn, plan and reason. Learn about knowledge representation and planning, machine learning and data mining, digital ethics and security analytics.
Cyber security
Discover how to create new technologies to improve existing security and minimise vulnerability in design systems.
Distributed computing
Learn how to manage complex networks of computers. Gain knowledge about cloud computing, mobile computer systems programming, high performance computing, distributed algorithms and parallel computing.
Human-computer interaction
Focus on human-centred design, development and interactive technologies. Gain skills in design thinking, user-centred evaluation, social computing, information architecture and ubiquitous computing.
Learn more about FEIT specialisations
Internships and industry experience
Running over 10–15 weeks, you could intern at a technology, banking and finance, health or telecommunications company in our Internship subject.
Research subjects
Undertake an in-depth research investigation, collaborating with our world leading researchers. Depending on your specialisation, you can choose between the subjects Computing Project, HCI Project and Spatial IT Project.
Explore this course
Explore the subjects you could choose as part of this degree.
200 point program
Select a specialisation:
Foundation
Students must complete 50 credit points:
| Accordion | |
|---|---|
| Internet Technologies · 12.5 pts |
AIMS The subject will introduce the basics of computer networks to students through a study of layered models of computer networks and applications. The first half of the subject deals with data communication protocols in the lower layers of OSI and TCP/IP reference models. The students will be exposed to the working of various fundamental networking technologies such as wireless, LAN, RFID and sensor networks. The second half of the subject deals with the upper layers of the TCP/IP reference model through a study of several Internet applications. INDICATIVE CONTENT Topics covered include: Introduction to Internet, OSI reference model layers, protocols and services, data transmission basics, interface standards, network topologies, data link protocols, message routing, LANs, WANs, TCP/IP suite, detailed study of common network applications (e.g., email, news, FTP, Web), network management, and current and future developments in network hardware and protocols. |
| Algorithms and Complexity · 12.5 pts |
AIMS The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order. INDICATIVE CONTENT Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power. |
| Programming and Software Development · 12.5 pts |
AIMS The aim for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java. INDICATIVE CONTENT Topics covered will include:
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| Database Systems & Data Modelling · 12.5 pts |
AIMS The subject introduces key topics in modern information organisation, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organisation and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalisation and de-normalisation; the SQL language; data integrity; transaction processing, data warehousing, web services and organisational memory technologies. This is a core foundation subject for both the Master of Information Systems and Master of Information Technology. INDICATIVE CONTENT This subject serves as an introduction to databases and data modelling from a data management perspective. Database design, from conceptual design through to physical implementation will be covered. This will include Entity Relationship modelling, normalisation and de-normalisation and SQL. Additionally the use of databases in various contexts will be explored (web based databases, connecting programs to databases, data warehousing, health contexts, geospatial databases). |
Core
Students must complete 25 credit points:
| Accordion | |
|---|---|
| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
Group A electives
Students must complete 25 credit points from Group A and Group B, with a minimum of 12.5 credit points from Group A
| Accordion | |
|---|---|
| Natural Language Processing · 12.5 pts |
AIMS Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| Cryptography and Security · 12.5 pts |
AIMS The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions. This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology. INDICATIVE CONTENT The subject will be made up of three parts:
A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos. Topics drawn from:
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| Programming Language Implementation · 12.5 pts |
AIMS Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles. INDICATIVE CONTENT The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation. |
| Declarative Programming · 12.5 pts |
AIMS Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques. INDICATIVE CONTENT
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| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| Digital Innovation & Technopreneurship · 12.5 pts |
AIMS In today’s digital world, the opportunities for innovation, and therefore entrepreneurs, are abundant. Hence, understanding the dynamic relationship between these two and how they interact to create commercially successful ventures has never been more critical. Innovation and entrepreneurship are complex topics widely debated in the business, education, and economics communities. This subject focuses on the nature of innovation in the rapidly evolving business landscape and considers what entrepreneurs need to create the climate for successful innovation. The subject is relevant to all students, whether they seek to be strategy consultants, budding entrepreneurs, or work as ‘intrapreneurs’ in large organisations. Students will discover in this subject that innovation is more than having great ideas and that entrepreneurs can emerge from diverse backgrounds and industries. The subject emphasises the proactive nature of innovation and ‘technopreneurship’, as it will focus on the role of technology, in driving digitally focused innovative entrepreneurial pursuits. Students will learn about the various behaviours, attitudes, values, and skills needed by the entrepreneur. By equipping students with the relevant knowledge and foundational professional skills, this subject aims to nurture an entrepreneurial mindset and inspire and prepare them for success in technology-driven industries by empowering them to create, build, sustain or advise innovative businesses in the digital era. INDICATIVE CONTENT The subject comprises four themes:
The subject involves advanced learning activities, including case-based, experiential, and team-based approaches. Students learn how to devise and pitch an innovation idea and then present it in written form as a startup planning report. They also develop the necessary professional skills, personal attributes and reflections to help them be successful on their entrepreneurial journey. |
| Hardware Accelerated Computing · 12.5 pts |
Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include:
As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application |
Group B electives
| Accordion | |
|---|---|
| Models of Computation · 12.5 pts |
AIMS Formal logic and discrete mathematics provide the theoretical foundations for computer science. This subject uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.
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| Software Modelling and Design · 12.5 pts |
AIMS To construct a software system, requirements must be analysed and modelled, and designs developed and evaluated; this subject teaches knowledge and skills needed for these tasks. This includes the development of static and dynamic models for aspects of both the problem space and the solution space. The emphasis here is on an Agile approach, and on techniques appropriate for object-oriented development. INDICATIVE CONTENT Topics covered include:
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Advanced core
Students must complete 12.5 credit points:
| Accordion | |
|---|---|
| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
Advanced project selectives
Students must complete 25 credit points:
| Accordion | |
|---|---|
| Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD. Enrolment in this subject requires a weighted average mark of 75 or above. Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability. By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found. |
| Software Project · 25 pts |
AIMS This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software. INDICATIVE CONTENT Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team. |
| Technology Innovation Project · 25 pts |
AIMSThis subject involves an in-depth investigation into a real-world problem, utilising a Design Thinking approach to identify technology-based solutions to the problem. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication. INDICATIVE CONTENTIndicative content includes design thinking methodology, customer & problem discovery, design ideation, development of innovative technology prototypes, and innovation presentations. |
Advanced electives
Students must complete 62.5 credit points:
| Accordion | |
|---|---|
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Sensor Networks and Applications · 12.5 pts |
AIMS Sensor networks are a key component of today’s increasingly pervasive computing technologies. In this subject, the aim is to develop an understanding of sensor network technologies from three different perspectives: sensing, communication, and computing (including hardware, software, and algorithms) and their applications. INDICATIVE CONTENT Topics covered include:
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| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Programming Language Implementation · 12.5 pts |
AIMS Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles. INDICATIVE CONTENT The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation. |
| Constraint Programming · 12.5 pts |
AIMS The aims for this subject is for students to develop an understanding of approaches to solving combinatorial optimization problems with computers, and to be able to demonstrate proficiency in modelling and solving programs using a high-level modelling language, and understanding of different solving technologies. The modelling language used is MiniZinc. INDICATIVE CONTENT Topics covered will include:
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| Advanced Database Systems · 12.5 pts |
AIMS Many applications require reliability in access to data, and data should not be lost even in the presence of hardware failures. The ability to retrieve and process the data very efficiently is also paramount even when multiple users access the data from remote sites simultaneously. With the increasing size of data used in these applications, advanced techniques for data management have emerged to make many such advanced requirements for access to data a reality. The subject covers the technologies used in advanced database systems that use these techniques. Topics covered will include: transactions, concurrency control, reliability, ACID properties, performance, indexing of both structured and unstructured data, query processing, and further topics on different database types and database architectures. INDICATIVE CONTENT Topics covered include:
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| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| Program Analysis and Transformation · 12.5 pts |
AIMS In the 1930s, Alan Turing and Konrad Zuse independently proposed designs of computing machines based on the idea that storage used for data and storage used for instructions be indistinguishable. This “stored-program” model formed the blueprint for all modern computers. The ability to treat programs as data turned out to be very powerful, as it meant that a program could be designed to read, generate, analyse and/or transform other programs, and even modify itself while running. This subject is concerned with meta-programs - programs that work on other programs, possibly generating programs as output. People routinely read, generate, analyse, test, and transform programs. For example, a programmer may look through code for potential buffer overruns, and may add runtime tests to avoid the security problems that could result. It is preferable, however, to automate such activity as far as we can, partly because that makes programmers more productive, and partly because computers generally are better at these tasks, avoiding human oversights and mistakes. This subject introduces the main techniques and applications of program analysis and transformation, including methods used by modern optimizing compilers and allied tools. INDICATIVE CONTENT
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| AI Planning for Autonomy · 12.5 pts |
AIMS The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability. INDICATIVE CONTENT Topics are drawn from the field of advanced artificial intelligence including:
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| Stream Computing and Applications · 12.5 pts |
AIM With exponential growth in data generated from sensor data streams, search engines, spam filters, medical services, online analysis of financial data streams, and so forth, there is demand for fast monitoring and storage of huge amounts of data in real-time. Traditional technologies were not aimed to such fast streams of data. Usually they required data to be stored and indexed before it could be processed. Stream computing was created to tackle those problems that require processing and classification of continuous, high volume of data streams. It is highly used on applications such as Twitter, Facebook, High Frequency Trading and so forth. This subject will focus on the algorithms and data structures behind the analysis and management of streams. Theoretical underpinnings are emphasized, with implementation of some fundamental algorithms. INDICATIVE CONTENT
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| Advanced Theoretical Computer Science · 12.5 pts |
AIMS At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing. INDICATIVE CONTENT
Example of assignment
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| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Creating Innovative Professionals · 12.5 pts |
This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to work in your chosen profession in contexts where determining what problem to work on is an important complement to knowing how to solve that problem. You will develop these understandings, insights and skills by working on two projects. In the first, they will work in multi-disciplinary teams on a strategically-important innovation challenge sponsored by an industry organisation. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Creating Innovative Professionals (CIP) and its companion subject, Creating Innovative Engineering ENGR90034 (CIE), are delivered by the University's Innovation Practice Program. To learn more about the Program, including the range of organizations that have participated as sponsors, examples of past projects and to hear students talk about their experiences in taking CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require students to maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. |
| Information Visualisation · 12.5 pts |
Information visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (such as GEOM90008 Spatial Data Management), and the processing of data (such as GEOM90006 Spatial Data Analytics). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design. The subject will cover: Fundamentals of information visualisation and data graphics; visual thinking, human sensing and perception; foundations of data graphics and cartography; graphical user interface design; human computer interaction and human centred design. The lectures are also supported with several labs to develop student experience in this domain. In the labs, some tools such as Tableau and R libraries will be used to create a wide range of spatial and non-spatial visualisations in order to present data and discover patterns. These tools will also be used to create interactivity on visualisations. In addition, different visualisation types will be discussed and critiqued for different purposes. These will empower you to visualise various data sets in an appropriate form based on identified communication needs. |
| Spatial Data Management · 12.5 pts |
This subject combines practical spatial data management with the underpinning theories of spatial and spatiotemporal data representation and handling from Geographic Information Science. Spatial information is answering ‘where’ and ‘when’ questions – which are fundamental in decision making in complex systems, be it in urban planning, traffic and infrastructure management, environmental management, public health and sustainability, or any other social, economic, and environmental context. The subject introduces foundations of effective, efficient, and large-scale spatial data management. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data, in a modern ecosystem of spatial databases interfacing a geographic information system. The knowledge acquired is fundamental for subsequent studies in spatial data analytics and visualisation, and is of particular relevance to people wishing to establish a career in the spatial information, the environmental, or the planning industry. It is also suited for every postgraduate student who is looking for solid skills with Geographic Information Systems. In this subject, we will discuss the intricacies of computational representation and management of spatial information. The subject takes a spatial database perspective to management of extensive spatial datasets. The subject will cover the modelling, loading, transformation, analysis, and retrieval of spatial data in spatial databases. The subject covers data representations (vector, raster, and network data); spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees. The subject exposes the students to the whole lifecycle of spatial data management in a team-based project. Please view this video for further information: Spatial Data Management |
| Hardware Accelerated Computing · 12.5 pts |
Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include:
As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application |
| Computational Genomics · 12.5 pts |
AIM The study of genomics is on the forefront of biology. Current laboratory technologies generate huge amounts of data and computational analysis is necessary to make sense of these data. This subject covers a broad range of approaches to the computational analysis of genomic data. Students will learn the theory behind a variety of different approaches to genomic analysis, and be introduced to key tools in current use, preparing them to use existing methods appropriately as well as developing new ways to analyse genomic data. Students will also have opportunities to apply their skills in workshops and assignments using both existing computational genomics tools and writing custom Python functions. Computational Genomics can be taken as an elective subject. It can also be taken by undergraduate students, exchange students and PhD students, subject to the written approval of the subject coordinator. INDICATIVE CONTENT This subject covers the computational analysis of several important forms of genomic data. Topics include computational resource management, reproducible research principles, genomics workflows, sequence alignment, genome annotation, parallel computing, metagenomics and single-cell sequencing. The subject domain rapidly progresses, and subject content is regularly revised and updated. Practical work includes writing bioinformatics functions with Python code, accessing genomics data repositories and using popular command-line tools. Please view this video for further information: Computational Genomics |
| Web Security · 12.5 pts |
AIMS The Internet pervades nearly every aspect of our lives, from banking through to dating, and onto our interactions with government. As more of our lives move online we face ever greater risks to our data and way of life from internet vulnerabilities and attacks. Web Security will examine the fundamentals behind common vulnerabilities and attacks, and will introduce students to ways of mitigating the risks associated with them. It will also examine some of the ethical challenges faced when evaluating security and disclosing vulnerabilities. INDICATIVE CONTENT The subject will examine some of the cyber security challenges faced during system implementation and deployment. In particular it will identity common attack vectors, covering in more detail some of the Open Web Application Security Project (OWASP) Top 10 list of web application vulnerabilities, which may include topics such as injection, cross‐site scripting, session hijacking, and cross‐site request forgery, amongst others. Where appropriate practical examples will be examined to relate theory to practice. The subject will discuss methods for mitigating the risks associated with such vulnerabilities, and may include discussions on distributed denial of service, input validation and sanitisation, penetration testing, and the associated ethical and legal constraints, automated vulnerability scanning, and web application firewalls. |
| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Applied High Performance Computing · 12.5 pts |
The use of physics-based computer simulation is a powerful tool in the scientific and engineering fields that allows for the investigation of phenomena that are often inaccessible by other means. As modern compute architectures continue to increase in terms of parallelism and power, so too can these simulations increase in scale and fidelity, but only when equipped with an understanding of the mathematics and underlying hardware, necessary to leverage this power. This subject will aim to develop such an understanding by tying together key tools and techniques used in the design of scientific software targeted at High Performance Computing (HPC) resources. This subject will introduce several numerical methods that are ubiquitous in the solution of ordinary differential equations (e.g. Euler and Runge-Kutta methods), partial differential equations (e.g. finite difference and finite element methods), linear systems (e.g. conjugate gradient method), and apply these tools to solve governing equations commonly found in areas such as fluid dynamics and thermodynamics. This subject will investigate the development of software targeting shared memory multicore architectures, distributed memory architectures with MPI, and GPU accelerators. |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Modelling Complex Software Systems · 12.5 pts |
AIMS |
| Modelling Complex Software Systems · 12.5 pts |
AIMS |
Foundation
Students must complete 50 credit points:
| Accordion | |
|---|---|
| Internet Technologies · 12.5 pts |
AIMS The subject will introduce the basics of computer networks to students through a study of layered models of computer networks and applications. The first half of the subject deals with data communication protocols in the lower layers of OSI and TCP/IP reference models. The students will be exposed to the working of various fundamental networking technologies such as wireless, LAN, RFID and sensor networks. The second half of the subject deals with the upper layers of the TCP/IP reference model through a study of several Internet applications. INDICATIVE CONTENT Topics covered include: Introduction to Internet, OSI reference model layers, protocols and services, data transmission basics, interface standards, network topologies, data link protocols, message routing, LANs, WANs, TCP/IP suite, detailed study of common network applications (e.g., email, news, FTP, Web), network management, and current and future developments in network hardware and protocols. |
| Algorithms and Complexity · 12.5 pts |
AIMS The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order. INDICATIVE CONTENT Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power. |
| Programming and Software Development · 12.5 pts |
AIMS The aim for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java. INDICATIVE CONTENT Topics covered will include:
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| Database Systems & Data Modelling · 12.5 pts |
AIMS The subject introduces key topics in modern information organisation, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organisation and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalisation and de-normalisation; the SQL language; data integrity; transaction processing, data warehousing, web services and organisational memory technologies. This is a core foundation subject for both the Master of Information Systems and Master of Information Technology. INDICATIVE CONTENT This subject serves as an introduction to databases and data modelling from a data management perspective. Database design, from conceptual design through to physical implementation will be covered. This will include Entity Relationship modelling, normalisation and de-normalisation and SQL. Additionally the use of databases in various contexts will be explored (web based databases, connecting programs to databases, data warehousing, health contexts, geospatial databases). |
Specialisation core
Students must complete 50 credit points:
| Accordion | |
|---|---|
| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
| AI Planning for Autonomy · 12.5 pts |
AIMS The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability. INDICATIVE CONTENT Topics are drawn from the field of advanced artificial intelligence including:
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| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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Advanced specialisation selectives
Students must complete between 50 and 75 credit points:
| Accordion | |
|---|---|
| Natural Language Processing · 12.5 pts |
AIMS Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| Advanced Algorithms and Data Structures · 12.5 pts |
Contemporary software systems such as search engines must deal with huge amounts of data, often in real time. In such cases, standard data structures and algorithms do not scale. This subject aims to provide an overview of contemporary advanced algorithms and data structures in computer science for such problems. These techniques serve as building blocks for solving complex algorithmic problems, and have many practical applications. |
| AI for Robotics · 12.5 pts |
AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including:
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| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| Computational Modelling and Simulation · 12.5 pts |
Computers are invaluable tools for modelling and simulating complex systems in a range of real-world domains. The complex behaviours exhibited by many biological, social, and technological systems - such as epidemics, urban systems, and robotics - challenge our ability to predict, analyse, and design such systems. Building computational models of these systems can help us better understand their structure and behaviour and make better decisions about their design and control. The aim of this subject is to provide students with a solid foundation in the conceptual and technical skills required to design, implement, and evaluate computational models of complex systems. INDICATIVE CONTENT Topics covered will be selected from:
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Program capstone
Students must complete 25 credit points:
| Accordion | |
|---|---|
| Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD. Enrolment in this subject requires a weighted average mark of 75 or above. Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability. By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found. |
| Software Project · 25 pts |
AIMS This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software. INDICATIVE CONTENT Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team. |
| Technology Innovation Project · 25 pts |
AIMSThis subject involves an in-depth investigation into a real-world problem, utilising a Design Thinking approach to identify technology-based solutions to the problem. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication. INDICATIVE CONTENTIndicative content includes design thinking methodology, customer & problem discovery, design ideation, development of innovative technology prototypes, and innovation presentations. |
Advanced CIS Electives
Students can choose a maximum of 25 credit points:
| Accordion | |
|---|---|
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Models of Computation · 12.5 pts |
AIMS Formal logic and discrete mathematics provide the theoretical foundations for computer science. This subject uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.
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| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Sensor Networks and Applications · 12.5 pts |
AIMS Sensor networks are a key component of today’s increasingly pervasive computing technologies. In this subject, the aim is to develop an understanding of sensor network technologies from three different perspectives: sensing, communication, and computing (including hardware, software, and algorithms) and their applications. INDICATIVE CONTENT Topics covered include:
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| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Cluster and Cloud Computing · 12.5 pts |
AIMS The growing popularity of the Internet along with the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we do parallel and distributed computing (PDC). Cluster and Cloud Computing are two approaches for PDC. Clusters employ cost-effective commodity components for building powerful computers within local-area networks. Recently, “cloud computing” has emerged as the new paradigm for delivery of computing as services in a pay-as-you-go-model via the Internet. These approaches are used to tackle may research problems with particular focus on "big data" challenges that arise across a variety of domains. Some examples of scientific and industrial applications that use these computing platforms are: system simulations, weather forecasting, climate prediction, automobile modelling and design, high-energy physics, movie rendering, business intelligence, big data computing, and delivering various business and consumer applications on a pay-as-you-go basis. This subject will enable students to understand these technologies, their goals, characteristics, and limitations, and develop both middleware supporting them and scalable applications supported by these platforms. This subject is an elective subject in the Master of Information Technology. It can also be taken as an Advanced Elective subject in the Master of Engineering (Software). INDICATIVE CONTENT
Please view this video for further information: Cluster and Cloud Computing |
| Cryptography and Security · 12.5 pts |
AIMS The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions. This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology. INDICATIVE CONTENT The subject will be made up of three parts:
A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos. Topics drawn from:
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| Constraint Programming · 12.5 pts |
AIMS The aims for this subject is for students to develop an understanding of approaches to solving combinatorial optimization problems with computers, and to be able to demonstrate proficiency in modelling and solving programs using a high-level modelling language, and understanding of different solving technologies. The modelling language used is MiniZinc. INDICATIVE CONTENT Topics covered will include:
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| Declarative Programming · 12.5 pts |
AIMS Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques. INDICATIVE CONTENT
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| Advanced Database Systems · 12.5 pts |
AIMS Many applications require reliability in access to data, and data should not be lost even in the presence of hardware failures. The ability to retrieve and process the data very efficiently is also paramount even when multiple users access the data from remote sites simultaneously. With the increasing size of data used in these applications, advanced techniques for data management have emerged to make many such advanced requirements for access to data a reality. The subject covers the technologies used in advanced database systems that use these techniques. Topics covered will include: transactions, concurrency control, reliability, ACID properties, performance, indexing of both structured and unstructured data, query processing, and further topics on different database types and database architectures. INDICATIVE CONTENT Topics covered include:
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| Advanced Theoretical Computer Science · 12.5 pts |
AIMS At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing. INDICATIVE CONTENT
Example of assignment
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| Trustworthy Machine Learning · 12.5 pts |
AIMS As machine learning systems are increasingly integrated into critical and data sensitive applications, ensuring their confidentiality, reliability, robustness, and fairness becomes imperative. The complexity of modern AI models, coupled with evolving threats such as data inference, adversarial attacks, data poisoning, and biases, necessitates new methodologies to build and evaluate trustworthy machine learning systems. Trustworthy Machine Learning will explore techniques to enhance the privacy, security, interpretability, and safety in deployment of machine learning models, ensuring they operate reliably in real-world environments. INDICATIVE CONTENT The subject will begin by introducing the key dimensions of trustworthiness in machine learning, including privacy, robustness, reliability, and fairness. Students will examine real-world case studies that highlight failures and vulnerabilities in deployed AI systems. The first part of the subject will explore different types of information leakage that can arise under various threat models and examine methods for protecting sensitive data during analysis. The second part will introduce machine learning techniques that enhance model reliability, with a particular focus on unsupervised learning methods such as anomaly detection, alarm correlation, and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats. By the end of the subject, students will gain both theoretical knowledge and practical skills to design, evaluate, and deploy machine learning models with trustworthiness as a core principle. |
| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Machine Learning Applications for Health · 12.5 pts |
Artificial Intelligence (AI) is an ever growing field that holds the promise to revolutionise the way we develop drugs, treat and manage patients. In particular, Machine Learning has become an important tool to make sense of clinical data that is routinely collected and stored as electronic health records to enable personalised medicine. This subject aims to introduce students to different AI applications in health, using different clinical data sources and computational techniques, discussing their idiosyncrasies and the main challenges in the area. INDICATIVE CONTENT Topics covered may include: supervised, unsupervised learning and their applications in health scenarios, interpretable machine learning in health, natural language processing in health, process mining, data sources and clinical information modelling, data wrangling, harmonising and filtering, clinical image data and deep learning. |
| Text Analytics for Health · 12.5 pts |
AIMS Text analytics (also known as natural language processing) is becoming increasingly important in clinical and public health given the near-ubiquitous adoption of text-based Electronic Health Record (EHR) systems in clinical care, and the widespread use of social media and online communities for health-related peer support. This subject aims to provide students with a grounding in applied health-related text analytics using a range of different data sources and application areas. INDICATIVE CONTENT Topics covered may include: introduction to text analytics and text analytics for health applications; introduction to healthcare and public health; development of text analytics pipelines for clinical notes; best practice in data annotation; clinical information extraction using rule-based methods and machine learning; knowledge resources for clinical text analytics; clinical text analytics toolkits; utilization of text analytics and social media for health applications; sentiment and stance analysis in social media data; data management, privacy, and ethical issues in health-related text analytics. |
| Technopreneurship Project · 25 pts |
This subject provides students from the Master of Information Technology and Master of Information Systems programs with hands-on experience in deep innovation investigation and technopreneurship. Working in groups, students will engage in customer discovery, concept ideation, and iterative validated learning through prototype feature development and testing, leading towards startup preparation. It offers a unique opportunity to design and implement an innovative digital product while developing a strategy for bringing the innovation to market as an entrepreneur. |
| Large Data Methods & Applications · 12.5 pts |
This course provides an introduction to an important contemporary statistical toolset for applications including data science, machine learning, signal processing, financial engineering, biomedical engineering, communication systems and other high-dimensional statistical applications. The course will cover topics including introduction to random matrix theory models in engineering; eigenvalue distributions; finite-dimensional and large-dimensional techniques, covariance estimation, principal component analysis and spectral clustering. These topics will be supplemented by applications across a range of traditional and emerging domains involving big data sets. |
| Hardware Accelerated Computing · 12.5 pts |
Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include:
As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application |
| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Design Innovation and Leadership · 12.5 pts |
A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects. In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. |
| Information Visualisation · 12.5 pts |
Information visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (such as GEOM90008 Spatial Data Management), and the processing of data (such as GEOM90006 Spatial Data Analytics). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design. The subject will cover: Fundamentals of information visualisation and data graphics; visual thinking, human sensing and perception; foundations of data graphics and cartography; graphical user interface design; human computer interaction and human centred design. The lectures are also supported with several labs to develop student experience in this domain. In the labs, some tools such as Tableau and R libraries will be used to create a wide range of spatial and non-spatial visualisations in order to present data and discover patterns. These tools will also be used to create interactivity on visualisations. In addition, different visualisation types will be discussed and critiqued for different purposes. These will empower you to visualise various data sets in an appropriate form based on identified communication needs. |
| Spatial Data Management · 12.5 pts |
This subject combines practical spatial data management with the underpinning theories of spatial and spatiotemporal data representation and handling from Geographic Information Science. Spatial information is answering ‘where’ and ‘when’ questions – which are fundamental in decision making in complex systems, be it in urban planning, traffic and infrastructure management, environmental management, public health and sustainability, or any other social, economic, and environmental context. The subject introduces foundations of effective, efficient, and large-scale spatial data management. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data, in a modern ecosystem of spatial databases interfacing a geographic information system. The knowledge acquired is fundamental for subsequent studies in spatial data analytics and visualisation, and is of particular relevance to people wishing to establish a career in the spatial information, the environmental, or the planning industry. It is also suited for every postgraduate student who is looking for solid skills with Geographic Information Systems. In this subject, we will discuss the intricacies of computational representation and management of spatial information. The subject takes a spatial database perspective to management of extensive spatial datasets. The subject will cover the modelling, loading, transformation, analysis, and retrieval of spatial data in spatial databases. The subject covers data representations (vector, raster, and network data); spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees. The subject exposes the students to the whole lifecycle of spatial data management in a team-based project. Please view this video for further information: Spatial Data Management |
| Advanced Imaging · 12.5 pts |
This subject will introduce students to advanced imaging technologies and the methods for extracting quantitative information from multi-source imagery. This subject builds on the knowledge of subjects such as imaging the environment, by considering multi-source images of the target to provide additional information such as the distance from the target to object from which a three-dimensional representation can be constructed. It also considers imaging of targets where illumination is provided by the instrument rather than natural light reflection or radiation from the target. Students who successfully complete this subject may find work in a variety of remote sensing or specialist consultancies or agencies. The techniques learnt may also be applied to other industries such as quality control in manufacturing or recording of archaeological sites. The subject is of particular relevance to students wishing to establish a career in infrastructure engineering, civil engineering, property management, surveying, spatial information and urban planning but is also relevant to a range of disciplines where 3D building information should be considered. |
| Human-Computer Interaction · 12.5 pts |
How do you design interactive technologies that are useful, usable and satisfying? How can we better understand user needs in order to inform the design of new technologies? Introduction to Human-Computer Interaction addresses these questions, and students will learn about the key theories, concepts and industry methods that are crucial to the user-centered design process. Indicative Content
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| Digital Innovation & Technopreneurship · 12.5 pts |
AIMS In today’s digital world, the opportunities for innovation, and therefore entrepreneurs, are abundant. Hence, understanding the dynamic relationship between these two and how they interact to create commercially successful ventures has never been more critical. Innovation and entrepreneurship are complex topics widely debated in the business, education, and economics communities. This subject focuses on the nature of innovation in the rapidly evolving business landscape and considers what entrepreneurs need to create the climate for successful innovation. The subject is relevant to all students, whether they seek to be strategy consultants, budding entrepreneurs, or work as ‘intrapreneurs’ in large organisations. Students will discover in this subject that innovation is more than having great ideas and that entrepreneurs can emerge from diverse backgrounds and industries. The subject emphasises the proactive nature of innovation and ‘technopreneurship’, as it will focus on the role of technology, in driving digitally focused innovative entrepreneurial pursuits. Students will learn about the various behaviours, attitudes, values, and skills needed by the entrepreneur. By equipping students with the relevant knowledge and foundational professional skills, this subject aims to nurture an entrepreneurial mindset and inspire and prepare them for success in technology-driven industries by empowering them to create, build, sustain or advise innovative businesses in the digital era. INDICATIVE CONTENT The subject comprises four themes:
The subject involves advanced learning activities, including case-based, experiential, and team-based approaches. Students learn how to devise and pitch an innovation idea and then present it in written form as a startup planning report. They also develop the necessary professional skills, personal attributes and reflections to help them be successful on their entrepreneurial journey. |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Modelling Complex Software Systems · 12.5 pts |
AIMS |
Foundation
Students must complete 50 credit points:
| Accordion | |
|---|---|
| Internet Technologies · 12.5 pts |
AIMS The subject will introduce the basics of computer networks to students through a study of layered models of computer networks and applications. The first half of the subject deals with data communication protocols in the lower layers of OSI and TCP/IP reference models. The students will be exposed to the working of various fundamental networking technologies such as wireless, LAN, RFID and sensor networks. The second half of the subject deals with the upper layers of the TCP/IP reference model through a study of several Internet applications. INDICATIVE CONTENT Topics covered include: Introduction to Internet, OSI reference model layers, protocols and services, data transmission basics, interface standards, network topologies, data link protocols, message routing, LANs, WANs, TCP/IP suite, detailed study of common network applications (e.g., email, news, FTP, Web), network management, and current and future developments in network hardware and protocols. |
| Algorithms and Complexity · 12.5 pts |
AIMS The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order. INDICATIVE CONTENT Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power. |
| Programming and Software Development · 12.5 pts |
AIMS The aim for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java. INDICATIVE CONTENT Topics covered will include:
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| Database Systems & Data Modelling · 12.5 pts |
AIMS The subject introduces key topics in modern information organisation, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organisation and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalisation and de-normalisation; the SQL language; data integrity; transaction processing, data warehousing, web services and organisational memory technologies. This is a core foundation subject for both the Master of Information Systems and Master of Information Technology. INDICATIVE CONTENT This subject serves as an introduction to databases and data modelling from a data management perspective. Database design, from conceptual design through to physical implementation will be covered. This will include Entity Relationship modelling, normalisation and de-normalisation and SQL. Additionally the use of databases in various contexts will be explored (web based databases, connecting programs to databases, data warehousing, health contexts, geospatial databases). |
Specialisation core
Students must complete 50 credit points:
| Accordion | |
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| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
| Cyber Security Management · 12.5 pts |
AIMS This subject introduces a range of information security management services implemented in industry. The subject will cover the fundamental principles and practice of security risk assessment, incident response and disaster recovery, knowledge leakage, systems and network security, and policy and culture. Students will develop an appreciation for the kinds of security practices that exist in industry in each of these areas. This subject supports course-level objectives by allowing students to have in-depth knowledge of the specialist area of information security management. The subject’s assessment tasks include five quizzes continuously testing knowledge of cybersecurity management delivered in class, a group research assignment assessed in oral format, and an individual closed-book examination. These tasks will encourage students to develop a high level of achievement in critical thinking, research activities, presentation skills, and the practical application of cybersecurity management principles in organizational contexts. INDICATIVE CONTENT Security principles and techniques discussed are: Models for understanding knowledge leakage, Security Risk Assessment Methods, Firewall and virtual private network (VPN) security scenarios, and Incident Response Methodology. Real world cases will be drawn from a range of organization types including critical infrastructure installations in Australia. |
| Web Security · 12.5 pts |
AIMS The Internet pervades nearly every aspect of our lives, from banking through to dating, and onto our interactions with government. As more of our lives move online we face ever greater risks to our data and way of life from internet vulnerabilities and attacks. Web Security will examine the fundamentals behind common vulnerabilities and attacks, and will introduce students to ways of mitigating the risks associated with them. It will also examine some of the ethical challenges faced when evaluating security and disclosing vulnerabilities. INDICATIVE CONTENT The subject will examine some of the cyber security challenges faced during system implementation and deployment. In particular it will identity common attack vectors, covering in more detail some of the Open Web Application Security Project (OWASP) Top 10 list of web application vulnerabilities, which may include topics such as injection, cross‐site scripting, session hijacking, and cross‐site request forgery, amongst others. Where appropriate practical examples will be examined to relate theory to practice. The subject will discuss methods for mitigating the risks associated with such vulnerabilities, and may include discussions on distributed denial of service, input validation and sanitisation, penetration testing, and the associated ethical and legal constraints, automated vulnerability scanning, and web application firewalls. |
| Security & Software Testing · 12.5 pts |
AIMS Software is present in almost every part of our lives, and continues to change the world. Of importance to users is that software is correct, secure, reliable and efficient. The scale and complexity of most software ensures that achieving these qualities is non-trivial. This subject introduces students to the software engineering principles, processes, tools and techniques for analysing, measuring and developing correct, secure, and reliable software. The subject is one of the foundation subjects for the MC-ENG Master of Engineering (Software) and (Software with Business). INDICATIVE CONTENT Topics covered may include: methods for static and dynamic software testing; software security, quality and dependability; reliability measurement and engineering; performance measurement and engineering;software problem analysis and fault isolation; and software engineering tools. |
Advanced specialisation selectives
Students must complete between 50 and 75 credit points:
| Accordion | |
|---|---|
| Cryptography and Security · 12.5 pts |
AIMS The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions. This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology. INDICATIVE CONTENT The subject will be made up of three parts:
A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos. Topics drawn from:
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| Trustworthy Machine Learning · 12.5 pts |
AIMS As machine learning systems are increasingly integrated into critical and data sensitive applications, ensuring their confidentiality, reliability, robustness, and fairness becomes imperative. The complexity of modern AI models, coupled with evolving threats such as data inference, adversarial attacks, data poisoning, and biases, necessitates new methodologies to build and evaluate trustworthy machine learning systems. Trustworthy Machine Learning will explore techniques to enhance the privacy, security, interpretability, and safety in deployment of machine learning models, ensuring they operate reliably in real-world environments. INDICATIVE CONTENT The subject will begin by introducing the key dimensions of trustworthiness in machine learning, including privacy, robustness, reliability, and fairness. Students will examine real-world case studies that highlight failures and vulnerabilities in deployed AI systems. The first part of the subject will explore different types of information leakage that can arise under various threat models and examine methods for protecting sensitive data during analysis. The second part will introduce machine learning techniques that enhance model reliability, with a particular focus on unsupervised learning methods such as anomaly detection, alarm correlation, and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats. By the end of the subject, students will gain both theoretical knowledge and practical skills to design, evaluate, and deploy machine learning models with trustworthiness as a core principle. |
| Cyber Security Clinic · 12.5 pts |
This subject involves a mixture of classroom instruction and client-facing practice, in which students will work directly with not-for-profit and community organisations to help improve their cybersecurity practices and capabilities. Roughly the first half of the subject involves traditional lectures and tutorials in which students learn core cyber security principles and the conceptual frameworks for cyber security practice within organisations, with a focus on not-for-profit and community organisations and the unique threats that they face. The second half of the subject is primarily practical in nature, with students putting into practice the classroom knowledge by working directly with organisations to help improve their cyber security practice. Community and not-for-profit organisations are especially important because they are rich targets for cyber attacks yet often have limited resources to employ cyber security professionals or consultants. In the practical component of this subject, students will carry out tasks including asset inventory construction, cyber risk assessment, developing recommendations for and assessing the effectiveness of security controls, and developing cyber security training material. Students will work with client organisations under the supervision of a member of academic staff. The skills and knowledge students obtain, and the experience putting those into practice, will strengthen their employability. Indicative content covered in the classroom includes: Information security principles and threats overview; traditional information security controls and threat mitigations; ethics of information security practice; the threat landscape, with a focus on not-for-profit and community organisations; cybersecurity problem diagnosis; threat modelling and risk assessments; phishing and social engineering threats and controls; cyber security training and security behaviours; and misinformation and disinformation threats and mitigations. Entry to this subject requires permission from the subject coordinator. |
| High Integrity Systems Engineering · 12.5 pts |
AIMS High integrity systems are systems that must be engineered to a high level of dependability, that is, a high level of safety, security, reliability and performance. In this subject students will explore the aims, principles, techniques and tools that are used to analyse, design and implement dependable systems. INDICATIVE CONTENT Topics include: an introduction to high-integrity systems; safety critical systems and safety engineering; mathematical modelling of systems; fault tolerant systems design; design by contract; static verification; and model-based testing. |
| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Cybersecurity Practice in Organisations · 12.5 pts |
This subject presents a series of teaching cases to immerse students in the real-world context of cybersecurity in organisations. Through the teaching cases, students will confront challenges to the management practice of cybersecurity. The teaching cases will provoke robust student-led discussions that will disentangle complex cybersecurity management concepts and instill confidence and critical thinking in students and encourage them to express their own ideas. Teaching cases have been selected to address a wide range of management challenges and expose key risks that organisations face today. These include the risks of adopting a compliance culture, fragmentation in organisational structures, poor decision-making, inadequate skills and experience, and misperceptions about the role of the cybersecurity function. |
| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
Program capstone
Students must complete 25 credit points:
| Accordion | |
|---|---|
| Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD. Enrolment in this subject requires a weighted average mark of 75 or above. Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability. By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found. |
| Software Project · 25 pts |
AIMS This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software. INDICATIVE CONTENT Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team. |
| Technology Innovation Project · 25 pts |
AIMSThis subject involves an in-depth investigation into a real-world problem, utilising a Design Thinking approach to identify technology-based solutions to the problem. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication. INDICATIVE CONTENTIndicative content includes design thinking methodology, customer & problem discovery, design ideation, development of innovative technology prototypes, and innovation presentations. |
Advanced CIS electives
Students can choose a maximum of 25 credit points:
| Accordion | |
|---|---|
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| AI Planning for Autonomy · 12.5 pts |
AIMS The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability. INDICATIVE CONTENT Topics are drawn from the field of advanced artificial intelligence including:
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| Advanced Theoretical Computer Science · 12.5 pts |
AIMS At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing. INDICATIVE CONTENT
Example of assignment
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| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Human-Computer Interaction · 12.5 pts |
How do you design interactive technologies that are useful, usable and satisfying? How can we better understand user needs in order to inform the design of new technologies? Introduction to Human-Computer Interaction addresses these questions, and students will learn about the key theories, concepts and industry methods that are crucial to the user-centered design process. Indicative Content
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| Technopreneurship Project · 25 pts |
This subject provides students from the Master of Information Technology and Master of Information Systems programs with hands-on experience in deep innovation investigation and technopreneurship. Working in groups, students will engage in customer discovery, concept ideation, and iterative validated learning through prototype feature development and testing, leading towards startup preparation. It offers a unique opportunity to design and implement an innovative digital product while developing a strategy for bringing the innovation to market as an entrepreneur. |
| Design Innovation and Leadership · 12.5 pts |
A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects. In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. |
| High Integrity Systems Engineering · 12.5 pts |
AIMS High integrity systems are systems that must be engineered to a high level of dependability, that is, a high level of safety, security, reliability and performance. In this subject students will explore the aims, principles, techniques and tools that are used to analyse, design and implement dependable systems. INDICATIVE CONTENT Topics include: an introduction to high-integrity systems; safety critical systems and safety engineering; mathematical modelling of systems; fault tolerant systems design; design by contract; static verification; and model-based testing. |
Foundation
Students must complete 50 credit points:
| Accordion | |
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| Internet Technologies · 12.5 pts |
AIMS The subject will introduce the basics of computer networks to students through a study of layered models of computer networks and applications. The first half of the subject deals with data communication protocols in the lower layers of OSI and TCP/IP reference models. The students will be exposed to the working of various fundamental networking technologies such as wireless, LAN, RFID and sensor networks. The second half of the subject deals with the upper layers of the TCP/IP reference model through a study of several Internet applications. INDICATIVE CONTENT Topics covered include: Introduction to Internet, OSI reference model layers, protocols and services, data transmission basics, interface standards, network topologies, data link protocols, message routing, LANs, WANs, TCP/IP suite, detailed study of common network applications (e.g., email, news, FTP, Web), network management, and current and future developments in network hardware and protocols. |
| Algorithms and Complexity · 12.5 pts |
AIMS The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order. INDICATIVE CONTENT Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power. |
| Programming and Software Development · 12.5 pts |
AIMS The aim for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java. INDICATIVE CONTENT Topics covered will include:
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| Database Systems & Data Modelling · 12.5 pts |
AIMS The subject introduces key topics in modern information organisation, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organisation and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalisation and de-normalisation; the SQL language; data integrity; transaction processing, data warehousing, web services and organisational memory technologies. This is a core foundation subject for both the Master of Information Systems and Master of Information Technology. INDICATIVE CONTENT This subject serves as an introduction to databases and data modelling from a data management perspective. Database design, from conceptual design through to physical implementation will be covered. This will include Entity Relationship modelling, normalisation and de-normalisation and SQL. Additionally the use of databases in various contexts will be explored (web based databases, connecting programs to databases, data warehousing, health contexts, geospatial databases). |
Specialisation core
Students must complete 50 credit points:
| Accordion | |
|---|---|
| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Distributed Algorithms · 12.5 pts |
AIMS The Internet, World Wide Web, bank networks, mobile phone networks and many others are examples for Distributed Systems. Distributed Systems rely on a key set of algorithms and data structures to run efficiently and effectively. In this subject, we learn these key algorithms that professionals work with while dealing with various systems. Clock synchronization, leader election, mutual exclusion, and replication are just a few areas were multiple well known algorithms were developed during the evolution of the Distributed Computing paradigm. INDICATIVE CONTENT Topics covered include:
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| Parallel and Multicore Computing · 12.5 pts |
AIMS The subject aims to introduce students to parallel algorithms and their analysis. Fundamental principles of parallel computing are discussed. Various parallel architectures and programming platforms are introduced. Parallel algorithms for different architectures, as well as parallel algorithms addressing specific scientific problems are critically analysed. INDICATIVE CONTENT Topics include: principles of parallel computing, PRAM model, PRAM algorithms, parallel architectures, OpenMP, shared memory algorithms, systolic algorithms, parallel communication patterns, PVM/MPI, scientific applications, hypercube, graph embeddings and extended parallel computing models. |
Advanced specialisation selectives
Students must complete between 50 and 75 credit points:
| Accordion | |
|---|---|
| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Modelling Complex Software Systems · 12.5 pts |
AIMS |
| Sensor Networks and Applications · 12.5 pts |
AIMS Sensor networks are a key component of today’s increasingly pervasive computing technologies. In this subject, the aim is to develop an understanding of sensor network technologies from three different perspectives: sensing, communication, and computing (including hardware, software, and algorithms) and their applications. INDICATIVE CONTENT Topics covered include:
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| Cluster and Cloud Computing · 12.5 pts |
AIMS The growing popularity of the Internet along with the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we do parallel and distributed computing (PDC). Cluster and Cloud Computing are two approaches for PDC. Clusters employ cost-effective commodity components for building powerful computers within local-area networks. Recently, “cloud computing” has emerged as the new paradigm for delivery of computing as services in a pay-as-you-go-model via the Internet. These approaches are used to tackle may research problems with particular focus on "big data" challenges that arise across a variety of domains. Some examples of scientific and industrial applications that use these computing platforms are: system simulations, weather forecasting, climate prediction, automobile modelling and design, high-energy physics, movie rendering, business intelligence, big data computing, and delivering various business and consumer applications on a pay-as-you-go basis. This subject will enable students to understand these technologies, their goals, characteristics, and limitations, and develop both middleware supporting them and scalable applications supported by these platforms. This subject is an elective subject in the Master of Information Technology. It can also be taken as an Advanced Elective subject in the Master of Engineering (Software). INDICATIVE CONTENT
Please view this video for further information: Cluster and Cloud Computing |
| Stream Computing and Applications · 12.5 pts |
AIM With exponential growth in data generated from sensor data streams, search engines, spam filters, medical services, online analysis of financial data streams, and so forth, there is demand for fast monitoring and storage of huge amounts of data in real-time. Traditional technologies were not aimed to such fast streams of data. Usually they required data to be stored and indexed before it could be processed. Stream computing was created to tackle those problems that require processing and classification of continuous, high volume of data streams. It is highly used on applications such as Twitter, Facebook, High Frequency Trading and so forth. This subject will focus on the algorithms and data structures behind the analysis and management of streams. Theoretical underpinnings are emphasized, with implementation of some fundamental algorithms. INDICATIVE CONTENT
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| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Hardware Accelerated Computing · 12.5 pts |
Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include:
As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application |
| Applied High Performance Computing · 12.5 pts |
The use of physics-based computer simulation is a powerful tool in the scientific and engineering fields that allows for the investigation of phenomena that are often inaccessible by other means. As modern compute architectures continue to increase in terms of parallelism and power, so too can these simulations increase in scale and fidelity, but only when equipped with an understanding of the mathematics and underlying hardware, necessary to leverage this power. This subject will aim to develop such an understanding by tying together key tools and techniques used in the design of scientific software targeted at High Performance Computing (HPC) resources. This subject will introduce several numerical methods that are ubiquitous in the solution of ordinary differential equations (e.g. Euler and Runge-Kutta methods), partial differential equations (e.g. finite difference and finite element methods), linear systems (e.g. conjugate gradient method), and apply these tools to solve governing equations commonly found in areas such as fluid dynamics and thermodynamics. This subject will investigate the development of software targeting shared memory multicore architectures, distributed memory architectures with MPI, and GPU accelerators. |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Models of Computation · 12.5 pts |
AIMS Formal logic and discrete mathematics provide the theoretical foundations for computer science. This subject uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.
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| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
| Cryptography and Security · 12.5 pts |
AIMS The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions. This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology. INDICATIVE CONTENT The subject will be made up of three parts:
A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos. Topics drawn from:
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Program capstone
Students must complete 25 credit points:
| Accordion | |
|---|---|
| Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD. Enrolment in this subject requires a weighted average mark of 75 or above. Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability. By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found. |
| Software Project · 25 pts |
AIMS This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software. INDICATIVE CONTENT Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team. |
| Technology Innovation Project · 25 pts |
AIMSThis subject involves an in-depth investigation into a real-world problem, utilising a Design Thinking approach to identify technology-based solutions to the problem. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication. INDICATIVE CONTENTIndicative content includes design thinking methodology, customer & problem discovery, design ideation, development of innovative technology prototypes, and innovation presentations. |
Advanced CIS electives
Students can choose a maximum of 25 credit points:
| Accordion | |
|---|---|
| Natural Language Processing · 12.5 pts |
AIMS Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| Programming Language Implementation · 12.5 pts |
AIMS Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles. INDICATIVE CONTENT The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation. |
| Constraint Programming · 12.5 pts |
AIMS The aims for this subject is for students to develop an understanding of approaches to solving combinatorial optimization problems with computers, and to be able to demonstrate proficiency in modelling and solving programs using a high-level modelling language, and understanding of different solving technologies. The modelling language used is MiniZinc. INDICATIVE CONTENT Topics covered will include:
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| Declarative Programming · 12.5 pts |
AIMS Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques. INDICATIVE CONTENT
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| Advanced Database Systems · 12.5 pts |
AIMS Many applications require reliability in access to data, and data should not be lost even in the presence of hardware failures. The ability to retrieve and process the data very efficiently is also paramount even when multiple users access the data from remote sites simultaneously. With the increasing size of data used in these applications, advanced techniques for data management have emerged to make many such advanced requirements for access to data a reality. The subject covers the technologies used in advanced database systems that use these techniques. Topics covered will include: transactions, concurrency control, reliability, ACID properties, performance, indexing of both structured and unstructured data, query processing, and further topics on different database types and database architectures. INDICATIVE CONTENT Topics covered include:
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| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| Program Analysis and Transformation · 12.5 pts |
AIMS In the 1930s, Alan Turing and Konrad Zuse independently proposed designs of computing machines based on the idea that storage used for data and storage used for instructions be indistinguishable. This “stored-program” model formed the blueprint for all modern computers. The ability to treat programs as data turned out to be very powerful, as it meant that a program could be designed to read, generate, analyse and/or transform other programs, and even modify itself while running. This subject is concerned with meta-programs - programs that work on other programs, possibly generating programs as output. People routinely read, generate, analyse, test, and transform programs. For example, a programmer may look through code for potential buffer overruns, and may add runtime tests to avoid the security problems that could result. It is preferable, however, to automate such activity as far as we can, partly because that makes programmers more productive, and partly because computers generally are better at these tasks, avoiding human oversights and mistakes. This subject introduces the main techniques and applications of program analysis and transformation, including methods used by modern optimizing compilers and allied tools. INDICATIVE CONTENT
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| Digital Innovation & Technopreneurship · 12.5 pts |
AIMS In today’s digital world, the opportunities for innovation, and therefore entrepreneurs, are abundant. Hence, understanding the dynamic relationship between these two and how they interact to create commercially successful ventures has never been more critical. Innovation and entrepreneurship are complex topics widely debated in the business, education, and economics communities. This subject focuses on the nature of innovation in the rapidly evolving business landscape and considers what entrepreneurs need to create the climate for successful innovation. The subject is relevant to all students, whether they seek to be strategy consultants, budding entrepreneurs, or work as ‘intrapreneurs’ in large organisations. Students will discover in this subject that innovation is more than having great ideas and that entrepreneurs can emerge from diverse backgrounds and industries. The subject emphasises the proactive nature of innovation and ‘technopreneurship’, as it will focus on the role of technology, in driving digitally focused innovative entrepreneurial pursuits. Students will learn about the various behaviours, attitudes, values, and skills needed by the entrepreneur. By equipping students with the relevant knowledge and foundational professional skills, this subject aims to nurture an entrepreneurial mindset and inspire and prepare them for success in technology-driven industries by empowering them to create, build, sustain or advise innovative businesses in the digital era. INDICATIVE CONTENT The subject comprises four themes:
The subject involves advanced learning activities, including case-based, experiential, and team-based approaches. Students learn how to devise and pitch an innovation idea and then present it in written form as a startup planning report. They also develop the necessary professional skills, personal attributes and reflections to help them be successful on their entrepreneurial journey. |
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| AI Planning for Autonomy · 12.5 pts |
AIMS The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability. INDICATIVE CONTENT Topics are drawn from the field of advanced artificial intelligence including:
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| Advanced Theoretical Computer Science · 12.5 pts |
AIMS At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing. INDICATIVE CONTENT
Example of assignment
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| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Information Visualisation · 12.5 pts |
Information visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (such as GEOM90008 Spatial Data Management), and the processing of data (such as GEOM90006 Spatial Data Analytics). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design. The subject will cover: Fundamentals of information visualisation and data graphics; visual thinking, human sensing and perception; foundations of data graphics and cartography; graphical user interface design; human computer interaction and human centred design. The lectures are also supported with several labs to develop student experience in this domain. In the labs, some tools such as Tableau and R libraries will be used to create a wide range of spatial and non-spatial visualisations in order to present data and discover patterns. These tools will also be used to create interactivity on visualisations. In addition, different visualisation types will be discussed and critiqued for different purposes. These will empower you to visualise various data sets in an appropriate form based on identified communication needs. |
| Spatial Data Management · 12.5 pts |
This subject combines practical spatial data management with the underpinning theories of spatial and spatiotemporal data representation and handling from Geographic Information Science. Spatial information is answering ‘where’ and ‘when’ questions – which are fundamental in decision making in complex systems, be it in urban planning, traffic and infrastructure management, environmental management, public health and sustainability, or any other social, economic, and environmental context. The subject introduces foundations of effective, efficient, and large-scale spatial data management. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data, in a modern ecosystem of spatial databases interfacing a geographic information system. The knowledge acquired is fundamental for subsequent studies in spatial data analytics and visualisation, and is of particular relevance to people wishing to establish a career in the spatial information, the environmental, or the planning industry. It is also suited for every postgraduate student who is looking for solid skills with Geographic Information Systems. In this subject, we will discuss the intricacies of computational representation and management of spatial information. The subject takes a spatial database perspective to management of extensive spatial datasets. The subject will cover the modelling, loading, transformation, analysis, and retrieval of spatial data in spatial databases. The subject covers data representations (vector, raster, and network data); spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees. The subject exposes the students to the whole lifecycle of spatial data management in a team-based project. Please view this video for further information: Spatial Data Management |
| Computational Modelling and Simulation · 12.5 pts |
Computers are invaluable tools for modelling and simulating complex systems in a range of real-world domains. The complex behaviours exhibited by many biological, social, and technological systems - such as epidemics, urban systems, and robotics - challenge our ability to predict, analyse, and design such systems. Building computational models of these systems can help us better understand their structure and behaviour and make better decisions about their design and control. The aim of this subject is to provide students with a solid foundation in the conceptual and technical skills required to design, implement, and evaluate computational models of complex systems. INDICATIVE CONTENT Topics covered will be selected from:
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| AI for Robotics · 12.5 pts |
AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including:
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| Human-Computer Interaction · 12.5 pts |
How do you design interactive technologies that are useful, usable and satisfying? How can we better understand user needs in order to inform the design of new technologies? Introduction to Human-Computer Interaction addresses these questions, and students will learn about the key theories, concepts and industry methods that are crucial to the user-centered design process. Indicative Content
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| Technopreneurship Project · 25 pts |
This subject provides students from the Master of Information Technology and Master of Information Systems programs with hands-on experience in deep innovation investigation and technopreneurship. Working in groups, students will engage in customer discovery, concept ideation, and iterative validated learning through prototype feature development and testing, leading towards startup preparation. It offers a unique opportunity to design and implement an innovative digital product while developing a strategy for bringing the innovation to market as an entrepreneur. |
| Design Innovation and Leadership · 12.5 pts |
A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects. In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. |
Foundation
Students must complete 50 credit points:
| Accordion | |
|---|---|
| Internet Technologies · 12.5 pts |
AIMS The subject will introduce the basics of computer networks to students through a study of layered models of computer networks and applications. The first half of the subject deals with data communication protocols in the lower layers of OSI and TCP/IP reference models. The students will be exposed to the working of various fundamental networking technologies such as wireless, LAN, RFID and sensor networks. The second half of the subject deals with the upper layers of the TCP/IP reference model through a study of several Internet applications. INDICATIVE CONTENT Topics covered include: Introduction to Internet, OSI reference model layers, protocols and services, data transmission basics, interface standards, network topologies, data link protocols, message routing, LANs, WANs, TCP/IP suite, detailed study of common network applications (e.g., email, news, FTP, Web), network management, and current and future developments in network hardware and protocols. |
| Algorithms and Complexity · 12.5 pts |
AIMS The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order. INDICATIVE CONTENT Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power. |
| Programming and Software Development · 12.5 pts |
AIMS The aim for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java. INDICATIVE CONTENT Topics covered will include:
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| Database Systems & Data Modelling · 12.5 pts |
AIMS The subject introduces key topics in modern information organisation, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organisation and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalisation and de-normalisation; the SQL language; data integrity; transaction processing, data warehousing, web services and organisational memory technologies. This is a core foundation subject for both the Master of Information Systems and Master of Information Technology. INDICATIVE CONTENT This subject serves as an introduction to databases and data modelling from a data management perspective. Database design, from conceptual design through to physical implementation will be covered. This will include Entity Relationship modelling, normalisation and de-normalisation and SQL. Additionally the use of databases in various contexts will be explored (web based databases, connecting programs to databases, data warehousing, health contexts, geospatial databases). |
Specialisation core
Students must complete 50 credit points:
| Accordion | |
|---|---|
| Designing Novel Interactions · 12.5 pts |
New interaction technologies continuously expand the range of input and output methods available in human-computer interaction. Interaction is no longer limited to desktop computers, windows-based interfaces, or keyboards and mice. Interfaces now include tangible communication, mobile and ubiquitous devices, ambient displays and sensing in public spaces. Novel interactions require specific methods to enable their conception, design, evaluation and use in creating interactive systems. This subject will introduce a selection of different interaction media and examine the specific methods used to create interactive systems with them. Underlying these specific methods are general conceptual approaches to design that are focused on innovative or disruptive interactions between users and technology. Case studies will cover both fundamental research and industrial design practice. An emphasis is placed on developing the skills to critique and adapt different interface technologies and paradigms, to develop prototype systems, and evaluate new interactions to ensure that they meet their intended goals. This subject follows a flipped classroom model. The interactive lecture consists of practical activities and active learning tasks. Complementary teaching is provided through pre-recorded lectures. |
| Evaluating the User Experience · 12.5 pts |
User Experience (UX) means the way we respond to technology, including our practical, intellectual, emotional and affective responses. UX is widely recognised as a major determinant of successful technology outcomes, and it provides the design inspiration behind some of the most successful innovations in digital technologies that define the present era. This subject concerns the methods and techniques that are used to identify what characterises UX and how you can recognise, measure and evaluate it in a variety of contexts. This entails a deep understanding of the psychological and social theories underlying UX, combined with practical knowledge of the various industry methods and tools currently in use. In terms of practice, an emphasis is placed on learning the skills needed to design, justify and conduct appropriate evaluations, and the interpretation of findings. In terms of theory, special emphasis is placed on how to identify and evaluate the various facets of UX, across a range of social and work-based settings, and across a range of technologies. |
| Fieldwork for Design · 12.5 pts |
This subject introduces students to the theories and methods used to understand people and settings for designing technical systems. The subject will equip students with the knowledge and skills needed to gather information about people and activities, to understand the intended users of the systems, and to use the insights gained from this process to identify design requirements. This subject is for students interested in a career in user experience (UX) design, interaction design, service design, usability engineering, and human-computer interaction research. It will be of value to students aiming to work in all areas of information technology development and implementation. |
| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
Advanced specialisation selectives
Students must complete between 50 and 75 credit points:
| Accordion | |
|---|---|
| Advanced Interface Prototyping · 12.5 pts |
When designing for today’s devices, we still focus on smartphone and web applications. But what about the digital services and devices of tomorrow? This subject will explore design techniques and technologies for future technologies, which are increasingly moving away from traditional screen-based interaction. We will consider design strategies and implications for novel technology settings, such as interaction with artificial intelligence, virtual and augmented reality, smart homes, autonomous vehicles, and voice interfaces. Students will learn to extend their current design practices to cover off-screen interaction, while also learning modern prototyping techniques to envision future interactive technologies. This is a practical, project-based subject, in which students will follow an iterative design process for designing a new user experience. Lectures and workshops will offer practical tools, techniques, and processes for prototyping these experiences. |
| Graphics and Interaction · 12.5 pts |
AIMS This subject introduces technologies and theoretical foundations of computer graphics and human-computer interaction (HCI) along with the aspects of human perception and action that inform their applications. The subject emphasises the 2D and 3D computer graphics pipeline, from the geometric modelling to visual representation and interaction with virtual environments. Core topics include geometry representation, 3D transformations, illumination models, rendering algorithms, animation, and object interactions. These technologies form the basis for developing 3D game engines and interactive applications across platforms ranging from PCs to tablet computers, incorporating natural user interfaces (NUIs). Applications explore computer games, virtual and augmented reality, movie visual effects, and social applications such as metaverse. The subject also extends into immersive multimodal interaction. This subject supports course-level objectives by allowing students to develop analytical and technical skills essential for developing and implementing real-world solutions in computer graphics and interaction applications. INDICATIVE CONTENT Topics are drawn from computer graphics and human-computer interaction including:
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| Digital Innovation & Technopreneurship · 12.5 pts |
AIMS In today’s digital world, the opportunities for innovation, and therefore entrepreneurs, are abundant. Hence, understanding the dynamic relationship between these two and how they interact to create commercially successful ventures has never been more critical. Innovation and entrepreneurship are complex topics widely debated in the business, education, and economics communities. This subject focuses on the nature of innovation in the rapidly evolving business landscape and considers what entrepreneurs need to create the climate for successful innovation. The subject is relevant to all students, whether they seek to be strategy consultants, budding entrepreneurs, or work as ‘intrapreneurs’ in large organisations. Students will discover in this subject that innovation is more than having great ideas and that entrepreneurs can emerge from diverse backgrounds and industries. The subject emphasises the proactive nature of innovation and ‘technopreneurship’, as it will focus on the role of technology, in driving digitally focused innovative entrepreneurial pursuits. Students will learn about the various behaviours, attitudes, values, and skills needed by the entrepreneur. By equipping students with the relevant knowledge and foundational professional skills, this subject aims to nurture an entrepreneurial mindset and inspire and prepare them for success in technology-driven industries by empowering them to create, build, sustain or advise innovative businesses in the digital era. INDICATIVE CONTENT The subject comprises four themes:
The subject involves advanced learning activities, including case-based, experiential, and team-based approaches. Students learn how to devise and pitch an innovation idea and then present it in written form as a startup planning report. They also develop the necessary professional skills, personal attributes and reflections to help them be successful on their entrepreneurial journey. |
| Human-AI Interaction · 12.5 pts |
This subject is designed to equip students with the essential knowledge and skills required to apply Artificial Intelligence (AI) techniques to the design, development, and evaluation of interactive technologies. The subject builds upon the foundational subjects in Human Computer Interaction (HCI) subjects, providing students with a deeper understanding of the role of AI in enhancing user experiences and designing intelligent interactive systems. Students will learn to apply AI algorithms and methodologies to enhance user experiences, usability, and interaction design, gaining the skills necessary to design and implement AI-driven HCI solutions that meet user needs and preferences through practical projects and hands-on exercises. |
| Social Computing · 12.5 pts |
Social Computing is a field of study that investigates computing techniques and systems to support, mediate, and understand aspects of social behaviours. Understanding the principles and foundations of Social Computing is important because of the rapid proliferation of social systems, particularly those aimed at end-users (e.g. social networking websites, crowd sourcing platforms, knowledge sharing platforms, etc.). This subject will introduce you to key concepts and principles of Social Computing, and provide you with training to investigate how these systems influence human behaviours, how to improve current implementations, and how to identify ways to better support social activities and interactions. |
| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
Program capstone
Students must complete 25 credit points:
| Accordion | |
|---|---|
| HCI Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Human-Computer Interaction or a related discipline. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. Under the supervision and guidance of an academic researcher, students are required to design and conduct a research investigation. This would typically involve a literature review, experimentation and data collection, and data analysis. The results will be reported as a thesis and in a public presentation. In some instances, it is expected that the results will also be submitted for publication in a conference or journal. |
| User Experience Design Project · 25 pts |
This subject gives students in the Human-Computer Interaction specialisation of the Master of Information Technology practical experience with the User Experience (UX) Design process. Students will take what they learned about fieldwork, prototyping, and evaluation, and apply it in a hands-on, industry-driven, UX project. You will be responsible for identifying and learning about potential stakeholders, exploring a range of design opportunities, and evaluating the success of your ideas. Along the way, you will produce relevant deliverables that support your project, communicate your ideas, and demonstrate your skills. At the end of the project, you will produce a portfolio of your work that you can use when applying for jobs and speaking to potential clients. From a brief, you will deconstruct and understand the scope of a client's idea. You will need to identify who their potential users are, how they currently solve similar problems, and what their expectations, challenges, and frustrations are. You will use data collection skills to capture the nuance of your different users and develop high-level requirements for the project. Next, you'll need to translate these requirements into initial sketches and subsequent high-fidelity prototypes (using industry standard tools). These prototypes will need to demonstrate how your ideas work and communicate the experience your users should expect. Finally, you will evaluate your prototypes, working with users to understand the opportunities and limitations of your design ideas. The deliverables you develop throughout this project, will form the foundations for your portfolio, where you concisely present your work, your learnings, and your reflections, to convince future employers that they should hire you. |
Advanced CIS electives
Students can choose a maximum of 25 credit points:
| Accordion | |
|---|---|
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Information Visualisation · 12.5 pts |
Information visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (such as GEOM90008 Spatial Data Management), and the processing of data (such as GEOM90006 Spatial Data Analytics). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design. The subject will cover: Fundamentals of information visualisation and data graphics; visual thinking, human sensing and perception; foundations of data graphics and cartography; graphical user interface design; human computer interaction and human centred design. The lectures are also supported with several labs to develop student experience in this domain. In the labs, some tools such as Tableau and R libraries will be used to create a wide range of spatial and non-spatial visualisations in order to present data and discover patterns. These tools will also be used to create interactivity on visualisations. In addition, different visualisation types will be discussed and critiqued for different purposes. These will empower you to visualise various data sets in an appropriate form based on identified communication needs. |
| Spatial Data Management · 12.5 pts |
This subject combines practical spatial data management with the underpinning theories of spatial and spatiotemporal data representation and handling from Geographic Information Science. Spatial information is answering ‘where’ and ‘when’ questions – which are fundamental in decision making in complex systems, be it in urban planning, traffic and infrastructure management, environmental management, public health and sustainability, or any other social, economic, and environmental context. The subject introduces foundations of effective, efficient, and large-scale spatial data management. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data, in a modern ecosystem of spatial databases interfacing a geographic information system. The knowledge acquired is fundamental for subsequent studies in spatial data analytics and visualisation, and is of particular relevance to people wishing to establish a career in the spatial information, the environmental, or the planning industry. It is also suited for every postgraduate student who is looking for solid skills with Geographic Information Systems. In this subject, we will discuss the intricacies of computational representation and management of spatial information. The subject takes a spatial database perspective to management of extensive spatial datasets. The subject will cover the modelling, loading, transformation, analysis, and retrieval of spatial data in spatial databases. The subject covers data representations (vector, raster, and network data); spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees. The subject exposes the students to the whole lifecycle of spatial data management in a team-based project. Please view this video for further information: Spatial Data Management |
| Knowledge Management Systems · 12.5 pts |
AIMS This subject focuses on how Knowledge Management (KM) and a range of Information Technologies and analysis techniques are used to support KM initiatives in organisations. Technologies likely to be considered are: collaborative and social media tools; corporate knowledge directories; data warehouses and other repositories of organizational memory; business intelligence including data-mining; process automation; workflow and document management. The emphasis is on high-level decision-making and the rationale of technology-based initiatives and their impact on organizational knowledge and its use. This subject supports course-level objectives by allowing students to develop analytical skills to understand the complexity of real-world KM work in organisations. It promotes innovative thinking around the deployment of existing and emerging information technologies for KM. The subject contributes to the development of independent critical inquiry, analysis and reflection. INDICATIVE CONTENT Techniques of analysis and design likely to be learned are: critical thinking, discourse analysis and design thinking. Real-world case studies in the form of fieldwork are conducted likely from the following domains: software industry; retail; creative/fashion industry; manufacturing; emergency management. Real case-study work will shape thinking about IT support for KM in these industries. |
| Digital Transformation of Health · 12.5 pts |
Healthcare is information intensive. Health data are generated, shared, consumed, and stored in a variety of partially overlapping complex networks. Healthcare lags behind many other sectors, despite efforts to use digital technologies to shape and improve health data and information processes since the middle of the 20th Century. The need for digital transformation of health is driven by socio-economic concerns (making healthcare more accessible and affordable) and patient safety (reducing medical errors, and redundant and ineffective interventions). This subject introduces the background, current state, and future opportunities of digital health. It provides a basic understanding of health and disease and how individuals experience both. It explores the nature of biomedical data, information, and knowledge - and how digital technologies are shaping the way these are used. Digital health technologies are examined from ethical, historical, technological, and psycho-social perspectives, considering positive and negative impacts. |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Human-Computer Interaction · 12.5 pts |
How do you design interactive technologies that are useful, usable and satisfying? How can we better understand user needs in order to inform the design of new technologies? Introduction to Human-Computer Interaction addresses these questions, and students will learn about the key theories, concepts and industry methods that are crucial to the user-centered design process. Indicative Content
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| Technopreneurship Project · 25 pts |
This subject provides students from the Master of Information Technology and Master of Information Systems programs with hands-on experience in deep innovation investigation and technopreneurship. Working in groups, students will engage in customer discovery, concept ideation, and iterative validated learning through prototype feature development and testing, leading towards startup preparation. It offers a unique opportunity to design and implement an innovative digital product while developing a strategy for bringing the innovation to market as an entrepreneur. |
| Design Innovation and Leadership · 12.5 pts |
A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects. In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. |
150 point program
Select a specialisation:
Core
Students must complete 25 credit points:
| Accordion | |
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| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
Group A electives
Students must complete 25 credit points from Group A and Group B, with a minimum of 12.5 credit points from Group A
| Accordion | |
|---|---|
| Natural Language Processing · 12.5 pts |
AIMS Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| Cryptography and Security · 12.5 pts |
AIMS The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions. This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology. INDICATIVE CONTENT The subject will be made up of three parts:
A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos. Topics drawn from:
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| Programming Language Implementation · 12.5 pts |
AIMS Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles. INDICATIVE CONTENT The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation. |
| Declarative Programming · 12.5 pts |
AIMS Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques. INDICATIVE CONTENT
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| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| Digital Innovation & Technopreneurship · 12.5 pts |
AIMS In today’s digital world, the opportunities for innovation, and therefore entrepreneurs, are abundant. Hence, understanding the dynamic relationship between these two and how they interact to create commercially successful ventures has never been more critical. Innovation and entrepreneurship are complex topics widely debated in the business, education, and economics communities. This subject focuses on the nature of innovation in the rapidly evolving business landscape and considers what entrepreneurs need to create the climate for successful innovation. The subject is relevant to all students, whether they seek to be strategy consultants, budding entrepreneurs, or work as ‘intrapreneurs’ in large organisations. Students will discover in this subject that innovation is more than having great ideas and that entrepreneurs can emerge from diverse backgrounds and industries. The subject emphasises the proactive nature of innovation and ‘technopreneurship’, as it will focus on the role of technology, in driving digitally focused innovative entrepreneurial pursuits. Students will learn about the various behaviours, attitudes, values, and skills needed by the entrepreneur. By equipping students with the relevant knowledge and foundational professional skills, this subject aims to nurture an entrepreneurial mindset and inspire and prepare them for success in technology-driven industries by empowering them to create, build, sustain or advise innovative businesses in the digital era. INDICATIVE CONTENT The subject comprises four themes:
The subject involves advanced learning activities, including case-based, experiential, and team-based approaches. Students learn how to devise and pitch an innovation idea and then present it in written form as a startup planning report. They also develop the necessary professional skills, personal attributes and reflections to help them be successful on their entrepreneurial journey. |
| Hardware Accelerated Computing · 12.5 pts |
Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include:
As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application |
Group B electives
| Accordion | |
|---|---|
| Models of Computation · 12.5 pts |
AIMS Formal logic and discrete mathematics provide the theoretical foundations for computer science. This subject uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.
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| Software Modelling and Design · 12.5 pts |
AIMS To construct a software system, requirements must be analysed and modelled, and designs developed and evaluated; this subject teaches knowledge and skills needed for these tasks. This includes the development of static and dynamic models for aspects of both the problem space and the solution space. The emphasis here is on an Agile approach, and on techniques appropriate for object-oriented development. INDICATIVE CONTENT Topics covered include:
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Advanced core
Students must complete 12.5 credit points:
| Accordion | |
|---|---|
| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
Advanced project selectives
Students must complete 25 credit points:
| Accordion | |
|---|---|
| Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD. Enrolment in this subject requires a weighted average mark of 75 or above. Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability. By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found. |
| Software Project · 25 pts |
AIMS This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software. INDICATIVE CONTENT Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team. |
| Technology Innovation Project · 25 pts |
AIMSThis subject involves an in-depth investigation into a real-world problem, utilising a Design Thinking approach to identify technology-based solutions to the problem. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication. INDICATIVE CONTENTIndicative content includes design thinking methodology, customer & problem discovery, design ideation, development of innovative technology prototypes, and innovation presentations. |
Advanced electives
Students must complete 62.5 credit points:
| Accordion | |
|---|---|
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Sensor Networks and Applications · 12.5 pts |
AIMS Sensor networks are a key component of today’s increasingly pervasive computing technologies. In this subject, the aim is to develop an understanding of sensor network technologies from three different perspectives: sensing, communication, and computing (including hardware, software, and algorithms) and their applications. INDICATIVE CONTENT Topics covered include:
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| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Programming Language Implementation · 12.5 pts |
AIMS Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles. INDICATIVE CONTENT The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation. |
| Constraint Programming · 12.5 pts |
AIMS The aims for this subject is for students to develop an understanding of approaches to solving combinatorial optimization problems with computers, and to be able to demonstrate proficiency in modelling and solving programs using a high-level modelling language, and understanding of different solving technologies. The modelling language used is MiniZinc. INDICATIVE CONTENT Topics covered will include:
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| Advanced Database Systems · 12.5 pts |
AIMS Many applications require reliability in access to data, and data should not be lost even in the presence of hardware failures. The ability to retrieve and process the data very efficiently is also paramount even when multiple users access the data from remote sites simultaneously. With the increasing size of data used in these applications, advanced techniques for data management have emerged to make many such advanced requirements for access to data a reality. The subject covers the technologies used in advanced database systems that use these techniques. Topics covered will include: transactions, concurrency control, reliability, ACID properties, performance, indexing of both structured and unstructured data, query processing, and further topics on different database types and database architectures. INDICATIVE CONTENT Topics covered include:
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| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| Program Analysis and Transformation · 12.5 pts |
AIMS In the 1930s, Alan Turing and Konrad Zuse independently proposed designs of computing machines based on the idea that storage used for data and storage used for instructions be indistinguishable. This “stored-program” model formed the blueprint for all modern computers. The ability to treat programs as data turned out to be very powerful, as it meant that a program could be designed to read, generate, analyse and/or transform other programs, and even modify itself while running. This subject is concerned with meta-programs - programs that work on other programs, possibly generating programs as output. People routinely read, generate, analyse, test, and transform programs. For example, a programmer may look through code for potential buffer overruns, and may add runtime tests to avoid the security problems that could result. It is preferable, however, to automate such activity as far as we can, partly because that makes programmers more productive, and partly because computers generally are better at these tasks, avoiding human oversights and mistakes. This subject introduces the main techniques and applications of program analysis and transformation, including methods used by modern optimizing compilers and allied tools. INDICATIVE CONTENT
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| AI Planning for Autonomy · 12.5 pts |
AIMS The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability. INDICATIVE CONTENT Topics are drawn from the field of advanced artificial intelligence including:
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| Stream Computing and Applications · 12.5 pts |
AIM With exponential growth in data generated from sensor data streams, search engines, spam filters, medical services, online analysis of financial data streams, and so forth, there is demand for fast monitoring and storage of huge amounts of data in real-time. Traditional technologies were not aimed to such fast streams of data. Usually they required data to be stored and indexed before it could be processed. Stream computing was created to tackle those problems that require processing and classification of continuous, high volume of data streams. It is highly used on applications such as Twitter, Facebook, High Frequency Trading and so forth. This subject will focus on the algorithms and data structures behind the analysis and management of streams. Theoretical underpinnings are emphasized, with implementation of some fundamental algorithms. INDICATIVE CONTENT
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| Advanced Theoretical Computer Science · 12.5 pts |
AIMS At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing. INDICATIVE CONTENT
Example of assignment
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| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Creating Innovative Professionals · 12.5 pts |
This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to work in your chosen profession in contexts where determining what problem to work on is an important complement to knowing how to solve that problem. You will develop these understandings, insights and skills by working on two projects. In the first, they will work in multi-disciplinary teams on a strategically-important innovation challenge sponsored by an industry organisation. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Creating Innovative Professionals (CIP) and its companion subject, Creating Innovative Engineering ENGR90034 (CIE), are delivered by the University's Innovation Practice Program. To learn more about the Program, including the range of organizations that have participated as sponsors, examples of past projects and to hear students talk about their experiences in taking CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require students to maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. |
| Information Visualisation · 12.5 pts |
Information visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (such as GEOM90008 Spatial Data Management), and the processing of data (such as GEOM90006 Spatial Data Analytics). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design. The subject will cover: Fundamentals of information visualisation and data graphics; visual thinking, human sensing and perception; foundations of data graphics and cartography; graphical user interface design; human computer interaction and human centred design. The lectures are also supported with several labs to develop student experience in this domain. In the labs, some tools such as Tableau and R libraries will be used to create a wide range of spatial and non-spatial visualisations in order to present data and discover patterns. These tools will also be used to create interactivity on visualisations. In addition, different visualisation types will be discussed and critiqued for different purposes. These will empower you to visualise various data sets in an appropriate form based on identified communication needs. |
| Spatial Data Management · 12.5 pts |
This subject combines practical spatial data management with the underpinning theories of spatial and spatiotemporal data representation and handling from Geographic Information Science. Spatial information is answering ‘where’ and ‘when’ questions – which are fundamental in decision making in complex systems, be it in urban planning, traffic and infrastructure management, environmental management, public health and sustainability, or any other social, economic, and environmental context. The subject introduces foundations of effective, efficient, and large-scale spatial data management. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data, in a modern ecosystem of spatial databases interfacing a geographic information system. The knowledge acquired is fundamental for subsequent studies in spatial data analytics and visualisation, and is of particular relevance to people wishing to establish a career in the spatial information, the environmental, or the planning industry. It is also suited for every postgraduate student who is looking for solid skills with Geographic Information Systems. In this subject, we will discuss the intricacies of computational representation and management of spatial information. The subject takes a spatial database perspective to management of extensive spatial datasets. The subject will cover the modelling, loading, transformation, analysis, and retrieval of spatial data in spatial databases. The subject covers data representations (vector, raster, and network data); spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees. The subject exposes the students to the whole lifecycle of spatial data management in a team-based project. Please view this video for further information: Spatial Data Management |
| Hardware Accelerated Computing · 12.5 pts |
Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include:
As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application |
| Computational Genomics · 12.5 pts |
AIM The study of genomics is on the forefront of biology. Current laboratory technologies generate huge amounts of data and computational analysis is necessary to make sense of these data. This subject covers a broad range of approaches to the computational analysis of genomic data. Students will learn the theory behind a variety of different approaches to genomic analysis, and be introduced to key tools in current use, preparing them to use existing methods appropriately as well as developing new ways to analyse genomic data. Students will also have opportunities to apply their skills in workshops and assignments using both existing computational genomics tools and writing custom Python functions. Computational Genomics can be taken as an elective subject. It can also be taken by undergraduate students, exchange students and PhD students, subject to the written approval of the subject coordinator. INDICATIVE CONTENT This subject covers the computational analysis of several important forms of genomic data. Topics include computational resource management, reproducible research principles, genomics workflows, sequence alignment, genome annotation, parallel computing, metagenomics and single-cell sequencing. The subject domain rapidly progresses, and subject content is regularly revised and updated. Practical work includes writing bioinformatics functions with Python code, accessing genomics data repositories and using popular command-line tools. Please view this video for further information: Computational Genomics |
| Web Security · 12.5 pts |
AIMS The Internet pervades nearly every aspect of our lives, from banking through to dating, and onto our interactions with government. As more of our lives move online we face ever greater risks to our data and way of life from internet vulnerabilities and attacks. Web Security will examine the fundamentals behind common vulnerabilities and attacks, and will introduce students to ways of mitigating the risks associated with them. It will also examine some of the ethical challenges faced when evaluating security and disclosing vulnerabilities. INDICATIVE CONTENT The subject will examine some of the cyber security challenges faced during system implementation and deployment. In particular it will identity common attack vectors, covering in more detail some of the Open Web Application Security Project (OWASP) Top 10 list of web application vulnerabilities, which may include topics such as injection, cross‐site scripting, session hijacking, and cross‐site request forgery, amongst others. Where appropriate practical examples will be examined to relate theory to practice. The subject will discuss methods for mitigating the risks associated with such vulnerabilities, and may include discussions on distributed denial of service, input validation and sanitisation, penetration testing, and the associated ethical and legal constraints, automated vulnerability scanning, and web application firewalls. |
| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Applied High Performance Computing · 12.5 pts |
The use of physics-based computer simulation is a powerful tool in the scientific and engineering fields that allows for the investigation of phenomena that are often inaccessible by other means. As modern compute architectures continue to increase in terms of parallelism and power, so too can these simulations increase in scale and fidelity, but only when equipped with an understanding of the mathematics and underlying hardware, necessary to leverage this power. This subject will aim to develop such an understanding by tying together key tools and techniques used in the design of scientific software targeted at High Performance Computing (HPC) resources. This subject will introduce several numerical methods that are ubiquitous in the solution of ordinary differential equations (e.g. Euler and Runge-Kutta methods), partial differential equations (e.g. finite difference and finite element methods), linear systems (e.g. conjugate gradient method), and apply these tools to solve governing equations commonly found in areas such as fluid dynamics and thermodynamics. This subject will investigate the development of software targeting shared memory multicore architectures, distributed memory architectures with MPI, and GPU accelerators. |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Modelling Complex Software Systems · 12.5 pts |
AIMS |
Specialisation core
Students must complete 50 credit points:
| Accordion | |
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| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
| AI Planning for Autonomy · 12.5 pts |
AIMS The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability. INDICATIVE CONTENT Topics are drawn from the field of advanced artificial intelligence including:
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| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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Advanced specialisation selectives
Students must complete between 50 and 75 credit points:
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| Natural Language Processing · 12.5 pts |
AIMS Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| Advanced Algorithms and Data Structures · 12.5 pts |
Contemporary software systems such as search engines must deal with huge amounts of data, often in real time. In such cases, standard data structures and algorithms do not scale. This subject aims to provide an overview of contemporary advanced algorithms and data structures in computer science for such problems. These techniques serve as building blocks for solving complex algorithmic problems, and have many practical applications. |
| AI for Robotics · 12.5 pts |
AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including:
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| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| Computational Modelling and Simulation · 12.5 pts |
Computers are invaluable tools for modelling and simulating complex systems in a range of real-world domains. The complex behaviours exhibited by many biological, social, and technological systems - such as epidemics, urban systems, and robotics - challenge our ability to predict, analyse, and design such systems. Building computational models of these systems can help us better understand their structure and behaviour and make better decisions about their design and control. The aim of this subject is to provide students with a solid foundation in the conceptual and technical skills required to design, implement, and evaluate computational models of complex systems. INDICATIVE CONTENT Topics covered will be selected from:
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Program capstone
Students must complete 25 credit points:
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| Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD. Enrolment in this subject requires a weighted average mark of 75 or above. Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability. By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found. |
| Software Project · 25 pts |
AIMS This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software. INDICATIVE CONTENT Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team. |
| Technology Innovation Project · 25 pts |
AIMSThis subject involves an in-depth investigation into a real-world problem, utilising a Design Thinking approach to identify technology-based solutions to the problem. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication. INDICATIVE CONTENTIndicative content includes design thinking methodology, customer & problem discovery, design ideation, development of innovative technology prototypes, and innovation presentations. |
Advanced CIS Electives
Students can choose a maximum of 25 credit points:
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| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Models of Computation · 12.5 pts |
AIMS Formal logic and discrete mathematics provide the theoretical foundations for computer science. This subject uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.
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| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Sensor Networks and Applications · 12.5 pts |
AIMS Sensor networks are a key component of today’s increasingly pervasive computing technologies. In this subject, the aim is to develop an understanding of sensor network technologies from three different perspectives: sensing, communication, and computing (including hardware, software, and algorithms) and their applications. INDICATIVE CONTENT Topics covered include:
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| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Cluster and Cloud Computing · 12.5 pts |
AIMS The growing popularity of the Internet along with the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we do parallel and distributed computing (PDC). Cluster and Cloud Computing are two approaches for PDC. Clusters employ cost-effective commodity components for building powerful computers within local-area networks. Recently, “cloud computing” has emerged as the new paradigm for delivery of computing as services in a pay-as-you-go-model via the Internet. These approaches are used to tackle may research problems with particular focus on "big data" challenges that arise across a variety of domains. Some examples of scientific and industrial applications that use these computing platforms are: system simulations, weather forecasting, climate prediction, automobile modelling and design, high-energy physics, movie rendering, business intelligence, big data computing, and delivering various business and consumer applications on a pay-as-you-go basis. This subject will enable students to understand these technologies, their goals, characteristics, and limitations, and develop both middleware supporting them and scalable applications supported by these platforms. This subject is an elective subject in the Master of Information Technology. It can also be taken as an Advanced Elective subject in the Master of Engineering (Software). INDICATIVE CONTENT
Please view this video for further information: Cluster and Cloud Computing |
| Cryptography and Security · 12.5 pts |
AIMS The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions. This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology. INDICATIVE CONTENT The subject will be made up of three parts:
A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos. Topics drawn from:
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| Constraint Programming · 12.5 pts |
AIMS The aims for this subject is for students to develop an understanding of approaches to solving combinatorial optimization problems with computers, and to be able to demonstrate proficiency in modelling and solving programs using a high-level modelling language, and understanding of different solving technologies. The modelling language used is MiniZinc. INDICATIVE CONTENT Topics covered will include:
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| Declarative Programming · 12.5 pts |
AIMS Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques. INDICATIVE CONTENT
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| Advanced Database Systems · 12.5 pts |
AIMS Many applications require reliability in access to data, and data should not be lost even in the presence of hardware failures. The ability to retrieve and process the data very efficiently is also paramount even when multiple users access the data from remote sites simultaneously. With the increasing size of data used in these applications, advanced techniques for data management have emerged to make many such advanced requirements for access to data a reality. The subject covers the technologies used in advanced database systems that use these techniques. Topics covered will include: transactions, concurrency control, reliability, ACID properties, performance, indexing of both structured and unstructured data, query processing, and further topics on different database types and database architectures. INDICATIVE CONTENT Topics covered include:
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| Advanced Theoretical Computer Science · 12.5 pts |
AIMS At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing. INDICATIVE CONTENT
Example of assignment
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| Trustworthy Machine Learning · 12.5 pts |
AIMS As machine learning systems are increasingly integrated into critical and data sensitive applications, ensuring their confidentiality, reliability, robustness, and fairness becomes imperative. The complexity of modern AI models, coupled with evolving threats such as data inference, adversarial attacks, data poisoning, and biases, necessitates new methodologies to build and evaluate trustworthy machine learning systems. Trustworthy Machine Learning will explore techniques to enhance the privacy, security, interpretability, and safety in deployment of machine learning models, ensuring they operate reliably in real-world environments. INDICATIVE CONTENT The subject will begin by introducing the key dimensions of trustworthiness in machine learning, including privacy, robustness, reliability, and fairness. Students will examine real-world case studies that highlight failures and vulnerabilities in deployed AI systems. The first part of the subject will explore different types of information leakage that can arise under various threat models and examine methods for protecting sensitive data during analysis. The second part will introduce machine learning techniques that enhance model reliability, with a particular focus on unsupervised learning methods such as anomaly detection, alarm correlation, and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats. By the end of the subject, students will gain both theoretical knowledge and practical skills to design, evaluate, and deploy machine learning models with trustworthiness as a core principle. |
| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Machine Learning Applications for Health · 12.5 pts |
Artificial Intelligence (AI) is an ever growing field that holds the promise to revolutionise the way we develop drugs, treat and manage patients. In particular, Machine Learning has become an important tool to make sense of clinical data that is routinely collected and stored as electronic health records to enable personalised medicine. This subject aims to introduce students to different AI applications in health, using different clinical data sources and computational techniques, discussing their idiosyncrasies and the main challenges in the area. INDICATIVE CONTENT Topics covered may include: supervised, unsupervised learning and their applications in health scenarios, interpretable machine learning in health, natural language processing in health, process mining, data sources and clinical information modelling, data wrangling, harmonising and filtering, clinical image data and deep learning. |
| Text Analytics for Health · 12.5 pts |
AIMS Text analytics (also known as natural language processing) is becoming increasingly important in clinical and public health given the near-ubiquitous adoption of text-based Electronic Health Record (EHR) systems in clinical care, and the widespread use of social media and online communities for health-related peer support. This subject aims to provide students with a grounding in applied health-related text analytics using a range of different data sources and application areas. INDICATIVE CONTENT Topics covered may include: introduction to text analytics and text analytics for health applications; introduction to healthcare and public health; development of text analytics pipelines for clinical notes; best practice in data annotation; clinical information extraction using rule-based methods and machine learning; knowledge resources for clinical text analytics; clinical text analytics toolkits; utilization of text analytics and social media for health applications; sentiment and stance analysis in social media data; data management, privacy, and ethical issues in health-related text analytics. |
| Technopreneurship Project · 25 pts |
This subject provides students from the Master of Information Technology and Master of Information Systems programs with hands-on experience in deep innovation investigation and technopreneurship. Working in groups, students will engage in customer discovery, concept ideation, and iterative validated learning through prototype feature development and testing, leading towards startup preparation. It offers a unique opportunity to design and implement an innovative digital product while developing a strategy for bringing the innovation to market as an entrepreneur. |
| Large Data Methods & Applications · 12.5 pts |
This course provides an introduction to an important contemporary statistical toolset for applications including data science, machine learning, signal processing, financial engineering, biomedical engineering, communication systems and other high-dimensional statistical applications. The course will cover topics including introduction to random matrix theory models in engineering; eigenvalue distributions; finite-dimensional and large-dimensional techniques, covariance estimation, principal component analysis and spectral clustering. These topics will be supplemented by applications across a range of traditional and emerging domains involving big data sets. |
| Hardware Accelerated Computing · 12.5 pts |
Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include:
As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application |
| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Design Innovation and Leadership · 12.5 pts |
A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects. In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. |
| Information Visualisation · 12.5 pts |
Information visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (such as GEOM90008 Spatial Data Management), and the processing of data (such as GEOM90006 Spatial Data Analytics). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design. The subject will cover: Fundamentals of information visualisation and data graphics; visual thinking, human sensing and perception; foundations of data graphics and cartography; graphical user interface design; human computer interaction and human centred design. The lectures are also supported with several labs to develop student experience in this domain. In the labs, some tools such as Tableau and R libraries will be used to create a wide range of spatial and non-spatial visualisations in order to present data and discover patterns. These tools will also be used to create interactivity on visualisations. In addition, different visualisation types will be discussed and critiqued for different purposes. These will empower you to visualise various data sets in an appropriate form based on identified communication needs. |
| Spatial Data Management · 12.5 pts |
This subject combines practical spatial data management with the underpinning theories of spatial and spatiotemporal data representation and handling from Geographic Information Science. Spatial information is answering ‘where’ and ‘when’ questions – which are fundamental in decision making in complex systems, be it in urban planning, traffic and infrastructure management, environmental management, public health and sustainability, or any other social, economic, and environmental context. The subject introduces foundations of effective, efficient, and large-scale spatial data management. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data, in a modern ecosystem of spatial databases interfacing a geographic information system. The knowledge acquired is fundamental for subsequent studies in spatial data analytics and visualisation, and is of particular relevance to people wishing to establish a career in the spatial information, the environmental, or the planning industry. It is also suited for every postgraduate student who is looking for solid skills with Geographic Information Systems. In this subject, we will discuss the intricacies of computational representation and management of spatial information. The subject takes a spatial database perspective to management of extensive spatial datasets. The subject will cover the modelling, loading, transformation, analysis, and retrieval of spatial data in spatial databases. The subject covers data representations (vector, raster, and network data); spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees. The subject exposes the students to the whole lifecycle of spatial data management in a team-based project. Please view this video for further information: Spatial Data Management |
| Advanced Imaging · 12.5 pts |
This subject will introduce students to advanced imaging technologies and the methods for extracting quantitative information from multi-source imagery. This subject builds on the knowledge of subjects such as imaging the environment, by considering multi-source images of the target to provide additional information such as the distance from the target to object from which a three-dimensional representation can be constructed. It also considers imaging of targets where illumination is provided by the instrument rather than natural light reflection or radiation from the target. Students who successfully complete this subject may find work in a variety of remote sensing or specialist consultancies or agencies. The techniques learnt may also be applied to other industries such as quality control in manufacturing or recording of archaeological sites. The subject is of particular relevance to students wishing to establish a career in infrastructure engineering, civil engineering, property management, surveying, spatial information and urban planning but is also relevant to a range of disciplines where 3D building information should be considered. |
| Human-Computer Interaction · 12.5 pts |
How do you design interactive technologies that are useful, usable and satisfying? How can we better understand user needs in order to inform the design of new technologies? Introduction to Human-Computer Interaction addresses these questions, and students will learn about the key theories, concepts and industry methods that are crucial to the user-centered design process. Indicative Content
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| Digital Innovation & Technopreneurship · 12.5 pts |
AIMS In today’s digital world, the opportunities for innovation, and therefore entrepreneurs, are abundant. Hence, understanding the dynamic relationship between these two and how they interact to create commercially successful ventures has never been more critical. Innovation and entrepreneurship are complex topics widely debated in the business, education, and economics communities. This subject focuses on the nature of innovation in the rapidly evolving business landscape and considers what entrepreneurs need to create the climate for successful innovation. The subject is relevant to all students, whether they seek to be strategy consultants, budding entrepreneurs, or work as ‘intrapreneurs’ in large organisations. Students will discover in this subject that innovation is more than having great ideas and that entrepreneurs can emerge from diverse backgrounds and industries. The subject emphasises the proactive nature of innovation and ‘technopreneurship’, as it will focus on the role of technology, in driving digitally focused innovative entrepreneurial pursuits. Students will learn about the various behaviours, attitudes, values, and skills needed by the entrepreneur. By equipping students with the relevant knowledge and foundational professional skills, this subject aims to nurture an entrepreneurial mindset and inspire and prepare them for success in technology-driven industries by empowering them to create, build, sustain or advise innovative businesses in the digital era. INDICATIVE CONTENT The subject comprises four themes:
The subject involves advanced learning activities, including case-based, experiential, and team-based approaches. Students learn how to devise and pitch an innovation idea and then present it in written form as a startup planning report. They also develop the necessary professional skills, personal attributes and reflections to help them be successful on their entrepreneurial journey. |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Modelling Complex Software Systems · 12.5 pts |
AIMS |
Specialisation core
Students must complete 50 credit points:
| Accordion | |
|---|---|
| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
| Cyber Security Management · 12.5 pts |
AIMS This subject introduces a range of information security management services implemented in industry. The subject will cover the fundamental principles and practice of security risk assessment, incident response and disaster recovery, knowledge leakage, systems and network security, and policy and culture. Students will develop an appreciation for the kinds of security practices that exist in industry in each of these areas. This subject supports course-level objectives by allowing students to have in-depth knowledge of the specialist area of information security management. The subject’s assessment tasks include five quizzes continuously testing knowledge of cybersecurity management delivered in class, a group research assignment assessed in oral format, and an individual closed-book examination. These tasks will encourage students to develop a high level of achievement in critical thinking, research activities, presentation skills, and the practical application of cybersecurity management principles in organizational contexts. INDICATIVE CONTENT Security principles and techniques discussed are: Models for understanding knowledge leakage, Security Risk Assessment Methods, Firewall and virtual private network (VPN) security scenarios, and Incident Response Methodology. Real world cases will be drawn from a range of organization types including critical infrastructure installations in Australia. |
| Web Security · 12.5 pts |
AIMS The Internet pervades nearly every aspect of our lives, from banking through to dating, and onto our interactions with government. As more of our lives move online we face ever greater risks to our data and way of life from internet vulnerabilities and attacks. Web Security will examine the fundamentals behind common vulnerabilities and attacks, and will introduce students to ways of mitigating the risks associated with them. It will also examine some of the ethical challenges faced when evaluating security and disclosing vulnerabilities. INDICATIVE CONTENT The subject will examine some of the cyber security challenges faced during system implementation and deployment. In particular it will identity common attack vectors, covering in more detail some of the Open Web Application Security Project (OWASP) Top 10 list of web application vulnerabilities, which may include topics such as injection, cross‐site scripting, session hijacking, and cross‐site request forgery, amongst others. Where appropriate practical examples will be examined to relate theory to practice. The subject will discuss methods for mitigating the risks associated with such vulnerabilities, and may include discussions on distributed denial of service, input validation and sanitisation, penetration testing, and the associated ethical and legal constraints, automated vulnerability scanning, and web application firewalls. |
| Security & Software Testing · 12.5 pts |
AIMS Software is present in almost every part of our lives, and continues to change the world. Of importance to users is that software is correct, secure, reliable and efficient. The scale and complexity of most software ensures that achieving these qualities is non-trivial. This subject introduces students to the software engineering principles, processes, tools and techniques for analysing, measuring and developing correct, secure, and reliable software. The subject is one of the foundation subjects for the MC-ENG Master of Engineering (Software) and (Software with Business). INDICATIVE CONTENT Topics covered may include: methods for static and dynamic software testing; software security, quality and dependability; reliability measurement and engineering; performance measurement and engineering;software problem analysis and fault isolation; and software engineering tools. |
Advanced specialisation selectives
Students must complete between 50 and 75 credit points:
| Accordion | |
|---|---|
| Cryptography and Security · 12.5 pts |
AIMS The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions. This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology. INDICATIVE CONTENT The subject will be made up of three parts:
A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos. Topics drawn from:
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| Trustworthy Machine Learning · 12.5 pts |
AIMS As machine learning systems are increasingly integrated into critical and data sensitive applications, ensuring their confidentiality, reliability, robustness, and fairness becomes imperative. The complexity of modern AI models, coupled with evolving threats such as data inference, adversarial attacks, data poisoning, and biases, necessitates new methodologies to build and evaluate trustworthy machine learning systems. Trustworthy Machine Learning will explore techniques to enhance the privacy, security, interpretability, and safety in deployment of machine learning models, ensuring they operate reliably in real-world environments. INDICATIVE CONTENT The subject will begin by introducing the key dimensions of trustworthiness in machine learning, including privacy, robustness, reliability, and fairness. Students will examine real-world case studies that highlight failures and vulnerabilities in deployed AI systems. The first part of the subject will explore different types of information leakage that can arise under various threat models and examine methods for protecting sensitive data during analysis. The second part will introduce machine learning techniques that enhance model reliability, with a particular focus on unsupervised learning methods such as anomaly detection, alarm correlation, and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats. By the end of the subject, students will gain both theoretical knowledge and practical skills to design, evaluate, and deploy machine learning models with trustworthiness as a core principle. |
| Cyber Security Clinic · 12.5 pts |
This subject involves a mixture of classroom instruction and client-facing practice, in which students will work directly with not-for-profit and community organisations to help improve their cybersecurity practices and capabilities. Roughly the first half of the subject involves traditional lectures and tutorials in which students learn core cyber security principles and the conceptual frameworks for cyber security practice within organisations, with a focus on not-for-profit and community organisations and the unique threats that they face. The second half of the subject is primarily practical in nature, with students putting into practice the classroom knowledge by working directly with organisations to help improve their cyber security practice. Community and not-for-profit organisations are especially important because they are rich targets for cyber attacks yet often have limited resources to employ cyber security professionals or consultants. In the practical component of this subject, students will carry out tasks including asset inventory construction, cyber risk assessment, developing recommendations for and assessing the effectiveness of security controls, and developing cyber security training material. Students will work with client organisations under the supervision of a member of academic staff. The skills and knowledge students obtain, and the experience putting those into practice, will strengthen their employability. Indicative content covered in the classroom includes: Information security principles and threats overview; traditional information security controls and threat mitigations; ethics of information security practice; the threat landscape, with a focus on not-for-profit and community organisations; cybersecurity problem diagnosis; threat modelling and risk assessments; phishing and social engineering threats and controls; cyber security training and security behaviours; and misinformation and disinformation threats and mitigations. Entry to this subject requires permission from the subject coordinator. |
| High Integrity Systems Engineering · 12.5 pts |
AIMS High integrity systems are systems that must be engineered to a high level of dependability, that is, a high level of safety, security, reliability and performance. In this subject students will explore the aims, principles, techniques and tools that are used to analyse, design and implement dependable systems. INDICATIVE CONTENT Topics include: an introduction to high-integrity systems; safety critical systems and safety engineering; mathematical modelling of systems; fault tolerant systems design; design by contract; static verification; and model-based testing. |
| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Cybersecurity Practice in Organisations · 12.5 pts |
This subject presents a series of teaching cases to immerse students in the real-world context of cybersecurity in organisations. Through the teaching cases, students will confront challenges to the management practice of cybersecurity. The teaching cases will provoke robust student-led discussions that will disentangle complex cybersecurity management concepts and instill confidence and critical thinking in students and encourage them to express their own ideas. Teaching cases have been selected to address a wide range of management challenges and expose key risks that organisations face today. These include the risks of adopting a compliance culture, fragmentation in organisational structures, poor decision-making, inadequate skills and experience, and misperceptions about the role of the cybersecurity function. |
| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
Program capstone
Students must complete 25 credit points:
| Accordion | |
|---|---|
| Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD. Enrolment in this subject requires a weighted average mark of 75 or above. Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability. By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found. |
| Software Project · 25 pts |
AIMS This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software. INDICATIVE CONTENT Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team. |
| Technology Innovation Project · 25 pts |
AIMSThis subject involves an in-depth investigation into a real-world problem, utilising a Design Thinking approach to identify technology-based solutions to the problem. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication. INDICATIVE CONTENTIndicative content includes design thinking methodology, customer & problem discovery, design ideation, development of innovative technology prototypes, and innovation presentations. |
Advanced CIS electives
Students can choose a maximum of 25 credit points:
| Accordion | |
|---|---|
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| AI Planning for Autonomy · 12.5 pts |
AIMS The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability. INDICATIVE CONTENT Topics are drawn from the field of advanced artificial intelligence including:
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| Advanced Theoretical Computer Science · 12.5 pts |
AIMS At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing. INDICATIVE CONTENT
Example of assignment
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| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Human-Computer Interaction · 12.5 pts |
How do you design interactive technologies that are useful, usable and satisfying? How can we better understand user needs in order to inform the design of new technologies? Introduction to Human-Computer Interaction addresses these questions, and students will learn about the key theories, concepts and industry methods that are crucial to the user-centered design process. Indicative Content
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| Technopreneurship Project · 25 pts |
This subject provides students from the Master of Information Technology and Master of Information Systems programs with hands-on experience in deep innovation investigation and technopreneurship. Working in groups, students will engage in customer discovery, concept ideation, and iterative validated learning through prototype feature development and testing, leading towards startup preparation. It offers a unique opportunity to design and implement an innovative digital product while developing a strategy for bringing the innovation to market as an entrepreneur. |
| Design Innovation and Leadership · 12.5 pts |
A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects. In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. |
| High Integrity Systems Engineering · 12.5 pts |
AIMS High integrity systems are systems that must be engineered to a high level of dependability, that is, a high level of safety, security, reliability and performance. In this subject students will explore the aims, principles, techniques and tools that are used to analyse, design and implement dependable systems. INDICATIVE CONTENT Topics include: an introduction to high-integrity systems; safety critical systems and safety engineering; mathematical modelling of systems; fault tolerant systems design; design by contract; static verification; and model-based testing. |
Specialisation core
Students must complete 50 credit points:
| Accordion | |
|---|---|
| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Distributed Algorithms · 12.5 pts |
AIMS The Internet, World Wide Web, bank networks, mobile phone networks and many others are examples for Distributed Systems. Distributed Systems rely on a key set of algorithms and data structures to run efficiently and effectively. In this subject, we learn these key algorithms that professionals work with while dealing with various systems. Clock synchronization, leader election, mutual exclusion, and replication are just a few areas were multiple well known algorithms were developed during the evolution of the Distributed Computing paradigm. INDICATIVE CONTENT Topics covered include:
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| Parallel and Multicore Computing · 12.5 pts |
AIMS The subject aims to introduce students to parallel algorithms and their analysis. Fundamental principles of parallel computing are discussed. Various parallel architectures and programming platforms are introduced. Parallel algorithms for different architectures, as well as parallel algorithms addressing specific scientific problems are critically analysed. INDICATIVE CONTENT Topics include: principles of parallel computing, PRAM model, PRAM algorithms, parallel architectures, OpenMP, shared memory algorithms, systolic algorithms, parallel communication patterns, PVM/MPI, scientific applications, hypercube, graph embeddings and extended parallel computing models. |
Advanced specialisation selectives
Students must complete between 50 and 75 credit points:
| Accordion | |
|---|---|
| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Modelling Complex Software Systems · 12.5 pts |
AIMS |
| Sensor Networks and Applications · 12.5 pts |
AIMS Sensor networks are a key component of today’s increasingly pervasive computing technologies. In this subject, the aim is to develop an understanding of sensor network technologies from three different perspectives: sensing, communication, and computing (including hardware, software, and algorithms) and their applications. INDICATIVE CONTENT Topics covered include:
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| Cluster and Cloud Computing · 12.5 pts |
AIMS The growing popularity of the Internet along with the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we do parallel and distributed computing (PDC). Cluster and Cloud Computing are two approaches for PDC. Clusters employ cost-effective commodity components for building powerful computers within local-area networks. Recently, “cloud computing” has emerged as the new paradigm for delivery of computing as services in a pay-as-you-go-model via the Internet. These approaches are used to tackle may research problems with particular focus on "big data" challenges that arise across a variety of domains. Some examples of scientific and industrial applications that use these computing platforms are: system simulations, weather forecasting, climate prediction, automobile modelling and design, high-energy physics, movie rendering, business intelligence, big data computing, and delivering various business and consumer applications on a pay-as-you-go basis. This subject will enable students to understand these technologies, their goals, characteristics, and limitations, and develop both middleware supporting them and scalable applications supported by these platforms. This subject is an elective subject in the Master of Information Technology. It can also be taken as an Advanced Elective subject in the Master of Engineering (Software). INDICATIVE CONTENT
Please view this video for further information: Cluster and Cloud Computing |
| Stream Computing and Applications · 12.5 pts |
AIM With exponential growth in data generated from sensor data streams, search engines, spam filters, medical services, online analysis of financial data streams, and so forth, there is demand for fast monitoring and storage of huge amounts of data in real-time. Traditional technologies were not aimed to such fast streams of data. Usually they required data to be stored and indexed before it could be processed. Stream computing was created to tackle those problems that require processing and classification of continuous, high volume of data streams. It is highly used on applications such as Twitter, Facebook, High Frequency Trading and so forth. This subject will focus on the algorithms and data structures behind the analysis and management of streams. Theoretical underpinnings are emphasized, with implementation of some fundamental algorithms. INDICATIVE CONTENT
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| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Hardware Accelerated Computing · 12.5 pts |
Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include:
As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application |
| Applied High Performance Computing · 12.5 pts |
The use of physics-based computer simulation is a powerful tool in the scientific and engineering fields that allows for the investigation of phenomena that are often inaccessible by other means. As modern compute architectures continue to increase in terms of parallelism and power, so too can these simulations increase in scale and fidelity, but only when equipped with an understanding of the mathematics and underlying hardware, necessary to leverage this power. This subject will aim to develop such an understanding by tying together key tools and techniques used in the design of scientific software targeted at High Performance Computing (HPC) resources. This subject will introduce several numerical methods that are ubiquitous in the solution of ordinary differential equations (e.g. Euler and Runge-Kutta methods), partial differential equations (e.g. finite difference and finite element methods), linear systems (e.g. conjugate gradient method), and apply these tools to solve governing equations commonly found in areas such as fluid dynamics and thermodynamics. This subject will investigate the development of software targeting shared memory multicore architectures, distributed memory architectures with MPI, and GPU accelerators. |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Models of Computation · 12.5 pts |
AIMS Formal logic and discrete mathematics provide the theoretical foundations for computer science. This subject uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.
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| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
| Cryptography and Security · 12.5 pts |
AIMS The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions. This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology. INDICATIVE CONTENT The subject will be made up of three parts:
A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos. Topics drawn from:
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Program capstone
Students must complete 25 credit points:
| Accordion | |
|---|---|
| Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD. Enrolment in this subject requires a weighted average mark of 75 or above. Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability. By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found. |
| Software Project · 25 pts |
AIMS This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software. INDICATIVE CONTENT Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team. |
| Technology Innovation Project · 25 pts |
AIMSThis subject involves an in-depth investigation into a real-world problem, utilising a Design Thinking approach to identify technology-based solutions to the problem. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication. INDICATIVE CONTENTIndicative content includes design thinking methodology, customer & problem discovery, design ideation, development of innovative technology prototypes, and innovation presentations. |
Advanced CIS electives
Students can choose a maximum of 25 credit points:
| Accordion | |
|---|---|
| Natural Language Processing · 12.5 pts |
AIMS Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| Programming Language Implementation · 12.5 pts |
AIMS Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles. INDICATIVE CONTENT The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation. |
| Constraint Programming · 12.5 pts |
AIMS The aims for this subject is for students to develop an understanding of approaches to solving combinatorial optimization problems with computers, and to be able to demonstrate proficiency in modelling and solving programs using a high-level modelling language, and understanding of different solving technologies. The modelling language used is MiniZinc. INDICATIVE CONTENT Topics covered will include:
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| Declarative Programming · 12.5 pts |
AIMS Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques. INDICATIVE CONTENT
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| Advanced Database Systems · 12.5 pts |
AIMS Many applications require reliability in access to data, and data should not be lost even in the presence of hardware failures. The ability to retrieve and process the data very efficiently is also paramount even when multiple users access the data from remote sites simultaneously. With the increasing size of data used in these applications, advanced techniques for data management have emerged to make many such advanced requirements for access to data a reality. The subject covers the technologies used in advanced database systems that use these techniques. Topics covered will include: transactions, concurrency control, reliability, ACID properties, performance, indexing of both structured and unstructured data, query processing, and further topics on different database types and database architectures. INDICATIVE CONTENT Topics covered include:
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| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| Program Analysis and Transformation · 12.5 pts |
AIMS In the 1930s, Alan Turing and Konrad Zuse independently proposed designs of computing machines based on the idea that storage used for data and storage used for instructions be indistinguishable. This “stored-program” model formed the blueprint for all modern computers. The ability to treat programs as data turned out to be very powerful, as it meant that a program could be designed to read, generate, analyse and/or transform other programs, and even modify itself while running. This subject is concerned with meta-programs - programs that work on other programs, possibly generating programs as output. People routinely read, generate, analyse, test, and transform programs. For example, a programmer may look through code for potential buffer overruns, and may add runtime tests to avoid the security problems that could result. It is preferable, however, to automate such activity as far as we can, partly because that makes programmers more productive, and partly because computers generally are better at these tasks, avoiding human oversights and mistakes. This subject introduces the main techniques and applications of program analysis and transformation, including methods used by modern optimizing compilers and allied tools. INDICATIVE CONTENT
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| Digital Innovation & Technopreneurship · 12.5 pts |
AIMS In today’s digital world, the opportunities for innovation, and therefore entrepreneurs, are abundant. Hence, understanding the dynamic relationship between these two and how they interact to create commercially successful ventures has never been more critical. Innovation and entrepreneurship are complex topics widely debated in the business, education, and economics communities. This subject focuses on the nature of innovation in the rapidly evolving business landscape and considers what entrepreneurs need to create the climate for successful innovation. The subject is relevant to all students, whether they seek to be strategy consultants, budding entrepreneurs, or work as ‘intrapreneurs’ in large organisations. Students will discover in this subject that innovation is more than having great ideas and that entrepreneurs can emerge from diverse backgrounds and industries. The subject emphasises the proactive nature of innovation and ‘technopreneurship’, as it will focus on the role of technology, in driving digitally focused innovative entrepreneurial pursuits. Students will learn about the various behaviours, attitudes, values, and skills needed by the entrepreneur. By equipping students with the relevant knowledge and foundational professional skills, this subject aims to nurture an entrepreneurial mindset and inspire and prepare them for success in technology-driven industries by empowering them to create, build, sustain or advise innovative businesses in the digital era. INDICATIVE CONTENT The subject comprises four themes:
The subject involves advanced learning activities, including case-based, experiential, and team-based approaches. Students learn how to devise and pitch an innovation idea and then present it in written form as a startup planning report. They also develop the necessary professional skills, personal attributes and reflections to help them be successful on their entrepreneurial journey. |
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| AI Planning for Autonomy · 12.5 pts |
AIMS The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability. INDICATIVE CONTENT Topics are drawn from the field of advanced artificial intelligence including:
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| Advanced Theoretical Computer Science · 12.5 pts |
AIMS At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing. INDICATIVE CONTENT
Example of assignment
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| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Information Visualisation · 12.5 pts |
Information visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (such as GEOM90008 Spatial Data Management), and the processing of data (such as GEOM90006 Spatial Data Analytics). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design. The subject will cover: Fundamentals of information visualisation and data graphics; visual thinking, human sensing and perception; foundations of data graphics and cartography; graphical user interface design; human computer interaction and human centred design. The lectures are also supported with several labs to develop student experience in this domain. In the labs, some tools such as Tableau and R libraries will be used to create a wide range of spatial and non-spatial visualisations in order to present data and discover patterns. These tools will also be used to create interactivity on visualisations. In addition, different visualisation types will be discussed and critiqued for different purposes. These will empower you to visualise various data sets in an appropriate form based on identified communication needs. |
| Spatial Data Management · 12.5 pts |
This subject combines practical spatial data management with the underpinning theories of spatial and spatiotemporal data representation and handling from Geographic Information Science. Spatial information is answering ‘where’ and ‘when’ questions – which are fundamental in decision making in complex systems, be it in urban planning, traffic and infrastructure management, environmental management, public health and sustainability, or any other social, economic, and environmental context. The subject introduces foundations of effective, efficient, and large-scale spatial data management. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data, in a modern ecosystem of spatial databases interfacing a geographic information system. The knowledge acquired is fundamental for subsequent studies in spatial data analytics and visualisation, and is of particular relevance to people wishing to establish a career in the spatial information, the environmental, or the planning industry. It is also suited for every postgraduate student who is looking for solid skills with Geographic Information Systems. In this subject, we will discuss the intricacies of computational representation and management of spatial information. The subject takes a spatial database perspective to management of extensive spatial datasets. The subject will cover the modelling, loading, transformation, analysis, and retrieval of spatial data in spatial databases. The subject covers data representations (vector, raster, and network data); spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees. The subject exposes the students to the whole lifecycle of spatial data management in a team-based project. Please view this video for further information: Spatial Data Management |
| Computational Modelling and Simulation · 12.5 pts |
Computers are invaluable tools for modelling and simulating complex systems in a range of real-world domains. The complex behaviours exhibited by many biological, social, and technological systems - such as epidemics, urban systems, and robotics - challenge our ability to predict, analyse, and design such systems. Building computational models of these systems can help us better understand their structure and behaviour and make better decisions about their design and control. The aim of this subject is to provide students with a solid foundation in the conceptual and technical skills required to design, implement, and evaluate computational models of complex systems. INDICATIVE CONTENT Topics covered will be selected from:
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| AI for Robotics · 12.5 pts |
AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including:
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| Human-Computer Interaction · 12.5 pts |
How do you design interactive technologies that are useful, usable and satisfying? How can we better understand user needs in order to inform the design of new technologies? Introduction to Human-Computer Interaction addresses these questions, and students will learn about the key theories, concepts and industry methods that are crucial to the user-centered design process. Indicative Content
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| Technopreneurship Project · 25 pts |
This subject provides students from the Master of Information Technology and Master of Information Systems programs with hands-on experience in deep innovation investigation and technopreneurship. Working in groups, students will engage in customer discovery, concept ideation, and iterative validated learning through prototype feature development and testing, leading towards startup preparation. It offers a unique opportunity to design and implement an innovative digital product while developing a strategy for bringing the innovation to market as an entrepreneur. |
| Design Innovation and Leadership · 12.5 pts |
A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects. In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. |
Specialisation core
Students must complete 50 credit points:
| Accordion | |
|---|---|
| Designing Novel Interactions · 12.5 pts |
New interaction technologies continuously expand the range of input and output methods available in human-computer interaction. Interaction is no longer limited to desktop computers, windows-based interfaces, or keyboards and mice. Interfaces now include tangible communication, mobile and ubiquitous devices, ambient displays and sensing in public spaces. Novel interactions require specific methods to enable their conception, design, evaluation and use in creating interactive systems. This subject will introduce a selection of different interaction media and examine the specific methods used to create interactive systems with them. Underlying these specific methods are general conceptual approaches to design that are focused on innovative or disruptive interactions between users and technology. Case studies will cover both fundamental research and industrial design practice. An emphasis is placed on developing the skills to critique and adapt different interface technologies and paradigms, to develop prototype systems, and evaluate new interactions to ensure that they meet their intended goals. This subject follows a flipped classroom model. The interactive lecture consists of practical activities and active learning tasks. Complementary teaching is provided through pre-recorded lectures. |
| Evaluating the User Experience · 12.5 pts |
User Experience (UX) means the way we respond to technology, including our practical, intellectual, emotional and affective responses. UX is widely recognised as a major determinant of successful technology outcomes, and it provides the design inspiration behind some of the most successful innovations in digital technologies that define the present era. This subject concerns the methods and techniques that are used to identify what characterises UX and how you can recognise, measure and evaluate it in a variety of contexts. This entails a deep understanding of the psychological and social theories underlying UX, combined with practical knowledge of the various industry methods and tools currently in use. In terms of practice, an emphasis is placed on learning the skills needed to design, justify and conduct appropriate evaluations, and the interpretation of findings. In terms of theory, special emphasis is placed on how to identify and evaluate the various facets of UX, across a range of social and work-based settings, and across a range of technologies. |
| Fieldwork for Design · 12.5 pts |
This subject introduces students to the theories and methods used to understand people and settings for designing technical systems. The subject will equip students with the knowledge and skills needed to gather information about people and activities, to understand the intended users of the systems, and to use the insights gained from this process to identify design requirements. This subject is for students interested in a career in user experience (UX) design, interaction design, service design, usability engineering, and human-computer interaction research. It will be of value to students aiming to work in all areas of information technology development and implementation. |
| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
Advanced specialisation selectives
Students must complete between 50 and 75 credit points:
| Accordion | |
|---|---|
| Advanced Interface Prototyping · 12.5 pts |
When designing for today’s devices, we still focus on smartphone and web applications. But what about the digital services and devices of tomorrow? This subject will explore design techniques and technologies for future technologies, which are increasingly moving away from traditional screen-based interaction. We will consider design strategies and implications for novel technology settings, such as interaction with artificial intelligence, virtual and augmented reality, smart homes, autonomous vehicles, and voice interfaces. Students will learn to extend their current design practices to cover off-screen interaction, while also learning modern prototyping techniques to envision future interactive technologies. This is a practical, project-based subject, in which students will follow an iterative design process for designing a new user experience. Lectures and workshops will offer practical tools, techniques, and processes for prototyping these experiences. |
| Graphics and Interaction · 12.5 pts |
AIMS This subject introduces technologies and theoretical foundations of computer graphics and human-computer interaction (HCI) along with the aspects of human perception and action that inform their applications. The subject emphasises the 2D and 3D computer graphics pipeline, from the geometric modelling to visual representation and interaction with virtual environments. Core topics include geometry representation, 3D transformations, illumination models, rendering algorithms, animation, and object interactions. These technologies form the basis for developing 3D game engines and interactive applications across platforms ranging from PCs to tablet computers, incorporating natural user interfaces (NUIs). Applications explore computer games, virtual and augmented reality, movie visual effects, and social applications such as metaverse. The subject also extends into immersive multimodal interaction. This subject supports course-level objectives by allowing students to develop analytical and technical skills essential for developing and implementing real-world solutions in computer graphics and interaction applications. INDICATIVE CONTENT Topics are drawn from computer graphics and human-computer interaction including:
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| Digital Innovation & Technopreneurship · 12.5 pts |
AIMS In today’s digital world, the opportunities for innovation, and therefore entrepreneurs, are abundant. Hence, understanding the dynamic relationship between these two and how they interact to create commercially successful ventures has never been more critical. Innovation and entrepreneurship are complex topics widely debated in the business, education, and economics communities. This subject focuses on the nature of innovation in the rapidly evolving business landscape and considers what entrepreneurs need to create the climate for successful innovation. The subject is relevant to all students, whether they seek to be strategy consultants, budding entrepreneurs, or work as ‘intrapreneurs’ in large organisations. Students will discover in this subject that innovation is more than having great ideas and that entrepreneurs can emerge from diverse backgrounds and industries. The subject emphasises the proactive nature of innovation and ‘technopreneurship’, as it will focus on the role of technology, in driving digitally focused innovative entrepreneurial pursuits. Students will learn about the various behaviours, attitudes, values, and skills needed by the entrepreneur. By equipping students with the relevant knowledge and foundational professional skills, this subject aims to nurture an entrepreneurial mindset and inspire and prepare them for success in technology-driven industries by empowering them to create, build, sustain or advise innovative businesses in the digital era. INDICATIVE CONTENT The subject comprises four themes:
The subject involves advanced learning activities, including case-based, experiential, and team-based approaches. Students learn how to devise and pitch an innovation idea and then present it in written form as a startup planning report. They also develop the necessary professional skills, personal attributes and reflections to help them be successful on their entrepreneurial journey. |
| Human-AI Interaction · 12.5 pts |
This subject is designed to equip students with the essential knowledge and skills required to apply Artificial Intelligence (AI) techniques to the design, development, and evaluation of interactive technologies. The subject builds upon the foundational subjects in Human Computer Interaction (HCI) subjects, providing students with a deeper understanding of the role of AI in enhancing user experiences and designing intelligent interactive systems. Students will learn to apply AI algorithms and methodologies to enhance user experiences, usability, and interaction design, gaining the skills necessary to design and implement AI-driven HCI solutions that meet user needs and preferences through practical projects and hands-on exercises. |
| Social Computing · 12.5 pts |
Social Computing is a field of study that investigates computing techniques and systems to support, mediate, and understand aspects of social behaviours. Understanding the principles and foundations of Social Computing is important because of the rapid proliferation of social systems, particularly those aimed at end-users (e.g. social networking websites, crowd sourcing platforms, knowledge sharing platforms, etc.). This subject will introduce you to key concepts and principles of Social Computing, and provide you with training to investigate how these systems influence human behaviours, how to improve current implementations, and how to identify ways to better support social activities and interactions. |
| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
Program capstone
Students must complete 25 credit points:
| Accordion | |
|---|---|
| HCI Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Human-Computer Interaction or a related discipline. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. Under the supervision and guidance of an academic researcher, students are required to design and conduct a research investigation. This would typically involve a literature review, experimentation and data collection, and data analysis. The results will be reported as a thesis and in a public presentation. In some instances, it is expected that the results will also be submitted for publication in a conference or journal. |
| User Experience Design Project · 25 pts |
This subject gives students in the Human-Computer Interaction specialisation of the Master of Information Technology practical experience with the User Experience (UX) Design process. Students will take what they learned about fieldwork, prototyping, and evaluation, and apply it in a hands-on, industry-driven, UX project. You will be responsible for identifying and learning about potential stakeholders, exploring a range of design opportunities, and evaluating the success of your ideas. Along the way, you will produce relevant deliverables that support your project, communicate your ideas, and demonstrate your skills. At the end of the project, you will produce a portfolio of your work that you can use when applying for jobs and speaking to potential clients. From a brief, you will deconstruct and understand the scope of a client's idea. You will need to identify who their potential users are, how they currently solve similar problems, and what their expectations, challenges, and frustrations are. You will use data collection skills to capture the nuance of your different users and develop high-level requirements for the project. Next, you'll need to translate these requirements into initial sketches and subsequent high-fidelity prototypes (using industry standard tools). These prototypes will need to demonstrate how your ideas work and communicate the experience your users should expect. Finally, you will evaluate your prototypes, working with users to understand the opportunities and limitations of your design ideas. The deliverables you develop throughout this project, will form the foundations for your portfolio, where you concisely present your work, your learnings, and your reflections, to convince future employers that they should hire you. |
Advanced CIS electives
Students can choose a maximum of 25 credit points:
| Accordion | |
|---|---|
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Information Visualisation · 12.5 pts |
Information visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (such as GEOM90008 Spatial Data Management), and the processing of data (such as GEOM90006 Spatial Data Analytics). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design. The subject will cover: Fundamentals of information visualisation and data graphics; visual thinking, human sensing and perception; foundations of data graphics and cartography; graphical user interface design; human computer interaction and human centred design. The lectures are also supported with several labs to develop student experience in this domain. In the labs, some tools such as Tableau and R libraries will be used to create a wide range of spatial and non-spatial visualisations in order to present data and discover patterns. These tools will also be used to create interactivity on visualisations. In addition, different visualisation types will be discussed and critiqued for different purposes. These will empower you to visualise various data sets in an appropriate form based on identified communication needs. |
| Spatial Data Management · 12.5 pts |
This subject combines practical spatial data management with the underpinning theories of spatial and spatiotemporal data representation and handling from Geographic Information Science. Spatial information is answering ‘where’ and ‘when’ questions – which are fundamental in decision making in complex systems, be it in urban planning, traffic and infrastructure management, environmental management, public health and sustainability, or any other social, economic, and environmental context. The subject introduces foundations of effective, efficient, and large-scale spatial data management. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data, in a modern ecosystem of spatial databases interfacing a geographic information system. The knowledge acquired is fundamental for subsequent studies in spatial data analytics and visualisation, and is of particular relevance to people wishing to establish a career in the spatial information, the environmental, or the planning industry. It is also suited for every postgraduate student who is looking for solid skills with Geographic Information Systems. In this subject, we will discuss the intricacies of computational representation and management of spatial information. The subject takes a spatial database perspective to management of extensive spatial datasets. The subject will cover the modelling, loading, transformation, analysis, and retrieval of spatial data in spatial databases. The subject covers data representations (vector, raster, and network data); spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees. The subject exposes the students to the whole lifecycle of spatial data management in a team-based project. Please view this video for further information: Spatial Data Management |
| Knowledge Management Systems · 12.5 pts |
AIMS This subject focuses on how Knowledge Management (KM) and a range of Information Technologies and analysis techniques are used to support KM initiatives in organisations. Technologies likely to be considered are: collaborative and social media tools; corporate knowledge directories; data warehouses and other repositories of organizational memory; business intelligence including data-mining; process automation; workflow and document management. The emphasis is on high-level decision-making and the rationale of technology-based initiatives and their impact on organizational knowledge and its use. This subject supports course-level objectives by allowing students to develop analytical skills to understand the complexity of real-world KM work in organisations. It promotes innovative thinking around the deployment of existing and emerging information technologies for KM. The subject contributes to the development of independent critical inquiry, analysis and reflection. INDICATIVE CONTENT Techniques of analysis and design likely to be learned are: critical thinking, discourse analysis and design thinking. Real-world case studies in the form of fieldwork are conducted likely from the following domains: software industry; retail; creative/fashion industry; manufacturing; emergency management. Real case-study work will shape thinking about IT support for KM in these industries. |
| Digital Transformation of Health · 12.5 pts |
Healthcare is information intensive. Health data are generated, shared, consumed, and stored in a variety of partially overlapping complex networks. Healthcare lags behind many other sectors, despite efforts to use digital technologies to shape and improve health data and information processes since the middle of the 20th Century. The need for digital transformation of health is driven by socio-economic concerns (making healthcare more accessible and affordable) and patient safety (reducing medical errors, and redundant and ineffective interventions). This subject introduces the background, current state, and future opportunities of digital health. It provides a basic understanding of health and disease and how individuals experience both. It explores the nature of biomedical data, information, and knowledge - and how digital technologies are shaping the way these are used. Digital health technologies are examined from ethical, historical, technological, and psycho-social perspectives, considering positive and negative impacts. |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Human-Computer Interaction · 12.5 pts |
How do you design interactive technologies that are useful, usable and satisfying? How can we better understand user needs in order to inform the design of new technologies? Introduction to Human-Computer Interaction addresses these questions, and students will learn about the key theories, concepts and industry methods that are crucial to the user-centered design process. Indicative Content
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| Technopreneurship Project · 25 pts |
This subject provides students from the Master of Information Technology and Master of Information Systems programs with hands-on experience in deep innovation investigation and technopreneurship. Working in groups, students will engage in customer discovery, concept ideation, and iterative validated learning through prototype feature development and testing, leading towards startup preparation. It offers a unique opportunity to design and implement an innovative digital product while developing a strategy for bringing the innovation to market as an entrepreneur. |
| Design Innovation and Leadership · 12.5 pts |
A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects. In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. |
100 point program
Select a specialisation:
Advanced core
Students must complete 12.5 credit points:
| Accordion | |
|---|---|
| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
Advanced project selectives
Students must complete 25 credit points:
| Accordion | |
|---|---|
| Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD. Enrolment in this subject requires a weighted average mark of 75 or above. Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability. By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found. |
| Software Project · 25 pts |
AIMS This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software. INDICATIVE CONTENT Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team. |
| Technology Innovation Project · 25 pts |
AIMSThis subject involves an in-depth investigation into a real-world problem, utilising a Design Thinking approach to identify technology-based solutions to the problem. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication. INDICATIVE CONTENTIndicative content includes design thinking methodology, customer & problem discovery, design ideation, development of innovative technology prototypes, and innovation presentations. |
Advanced electives
Students must complete 62.5 credit points:
| Accordion | |
|---|---|
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Sensor Networks and Applications · 12.5 pts |
AIMS Sensor networks are a key component of today’s increasingly pervasive computing technologies. In this subject, the aim is to develop an understanding of sensor network technologies from three different perspectives: sensing, communication, and computing (including hardware, software, and algorithms) and their applications. INDICATIVE CONTENT Topics covered include:
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| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Programming Language Implementation · 12.5 pts |
AIMS Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles. INDICATIVE CONTENT The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation. |
| Constraint Programming · 12.5 pts |
AIMS The aims for this subject is for students to develop an understanding of approaches to solving combinatorial optimization problems with computers, and to be able to demonstrate proficiency in modelling and solving programs using a high-level modelling language, and understanding of different solving technologies. The modelling language used is MiniZinc. INDICATIVE CONTENT Topics covered will include:
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| Advanced Database Systems · 12.5 pts |
AIMS Many applications require reliability in access to data, and data should not be lost even in the presence of hardware failures. The ability to retrieve and process the data very efficiently is also paramount even when multiple users access the data from remote sites simultaneously. With the increasing size of data used in these applications, advanced techniques for data management have emerged to make many such advanced requirements for access to data a reality. The subject covers the technologies used in advanced database systems that use these techniques. Topics covered will include: transactions, concurrency control, reliability, ACID properties, performance, indexing of both structured and unstructured data, query processing, and further topics on different database types and database architectures. INDICATIVE CONTENT Topics covered include:
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| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| Program Analysis and Transformation · 12.5 pts |
AIMS In the 1930s, Alan Turing and Konrad Zuse independently proposed designs of computing machines based on the idea that storage used for data and storage used for instructions be indistinguishable. This “stored-program” model formed the blueprint for all modern computers. The ability to treat programs as data turned out to be very powerful, as it meant that a program could be designed to read, generate, analyse and/or transform other programs, and even modify itself while running. This subject is concerned with meta-programs - programs that work on other programs, possibly generating programs as output. People routinely read, generate, analyse, test, and transform programs. For example, a programmer may look through code for potential buffer overruns, and may add runtime tests to avoid the security problems that could result. It is preferable, however, to automate such activity as far as we can, partly because that makes programmers more productive, and partly because computers generally are better at these tasks, avoiding human oversights and mistakes. This subject introduces the main techniques and applications of program analysis and transformation, including methods used by modern optimizing compilers and allied tools. INDICATIVE CONTENT
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| AI Planning for Autonomy · 12.5 pts |
AIMS The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability. INDICATIVE CONTENT Topics are drawn from the field of advanced artificial intelligence including:
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| Stream Computing and Applications · 12.5 pts |
AIM With exponential growth in data generated from sensor data streams, search engines, spam filters, medical services, online analysis of financial data streams, and so forth, there is demand for fast monitoring and storage of huge amounts of data in real-time. Traditional technologies were not aimed to such fast streams of data. Usually they required data to be stored and indexed before it could be processed. Stream computing was created to tackle those problems that require processing and classification of continuous, high volume of data streams. It is highly used on applications such as Twitter, Facebook, High Frequency Trading and so forth. This subject will focus on the algorithms and data structures behind the analysis and management of streams. Theoretical underpinnings are emphasized, with implementation of some fundamental algorithms. INDICATIVE CONTENT
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| Advanced Theoretical Computer Science · 12.5 pts |
AIMS At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing. INDICATIVE CONTENT
Example of assignment
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| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Creating Innovative Professionals · 12.5 pts |
This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to work in your chosen profession in contexts where determining what problem to work on is an important complement to knowing how to solve that problem. You will develop these understandings, insights and skills by working on two projects. In the first, they will work in multi-disciplinary teams on a strategically-important innovation challenge sponsored by an industry organisation. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Creating Innovative Professionals (CIP) and its companion subject, Creating Innovative Engineering ENGR90034 (CIE), are delivered by the University's Innovation Practice Program. To learn more about the Program, including the range of organizations that have participated as sponsors, examples of past projects and to hear students talk about their experiences in taking CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require students to maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. |
| Information Visualisation · 12.5 pts |
Information visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (such as GEOM90008 Spatial Data Management), and the processing of data (such as GEOM90006 Spatial Data Analytics). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design. The subject will cover: Fundamentals of information visualisation and data graphics; visual thinking, human sensing and perception; foundations of data graphics and cartography; graphical user interface design; human computer interaction and human centred design. The lectures are also supported with several labs to develop student experience in this domain. In the labs, some tools such as Tableau and R libraries will be used to create a wide range of spatial and non-spatial visualisations in order to present data and discover patterns. These tools will also be used to create interactivity on visualisations. In addition, different visualisation types will be discussed and critiqued for different purposes. These will empower you to visualise various data sets in an appropriate form based on identified communication needs. |
| Spatial Data Management · 12.5 pts |
This subject combines practical spatial data management with the underpinning theories of spatial and spatiotemporal data representation and handling from Geographic Information Science. Spatial information is answering ‘where’ and ‘when’ questions – which are fundamental in decision making in complex systems, be it in urban planning, traffic and infrastructure management, environmental management, public health and sustainability, or any other social, economic, and environmental context. The subject introduces foundations of effective, efficient, and large-scale spatial data management. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data, in a modern ecosystem of spatial databases interfacing a geographic information system. The knowledge acquired is fundamental for subsequent studies in spatial data analytics and visualisation, and is of particular relevance to people wishing to establish a career in the spatial information, the environmental, or the planning industry. It is also suited for every postgraduate student who is looking for solid skills with Geographic Information Systems. In this subject, we will discuss the intricacies of computational representation and management of spatial information. The subject takes a spatial database perspective to management of extensive spatial datasets. The subject will cover the modelling, loading, transformation, analysis, and retrieval of spatial data in spatial databases. The subject covers data representations (vector, raster, and network data); spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees. The subject exposes the students to the whole lifecycle of spatial data management in a team-based project. Please view this video for further information: Spatial Data Management |
| Hardware Accelerated Computing · 12.5 pts |
Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include:
As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application |
| Computational Genomics · 12.5 pts |
AIM The study of genomics is on the forefront of biology. Current laboratory technologies generate huge amounts of data and computational analysis is necessary to make sense of these data. This subject covers a broad range of approaches to the computational analysis of genomic data. Students will learn the theory behind a variety of different approaches to genomic analysis, and be introduced to key tools in current use, preparing them to use existing methods appropriately as well as developing new ways to analyse genomic data. Students will also have opportunities to apply their skills in workshops and assignments using both existing computational genomics tools and writing custom Python functions. Computational Genomics can be taken as an elective subject. It can also be taken by undergraduate students, exchange students and PhD students, subject to the written approval of the subject coordinator. INDICATIVE CONTENT This subject covers the computational analysis of several important forms of genomic data. Topics include computational resource management, reproducible research principles, genomics workflows, sequence alignment, genome annotation, parallel computing, metagenomics and single-cell sequencing. The subject domain rapidly progresses, and subject content is regularly revised and updated. Practical work includes writing bioinformatics functions with Python code, accessing genomics data repositories and using popular command-line tools. Please view this video for further information: Computational Genomics |
| Web Security · 12.5 pts |
AIMS The Internet pervades nearly every aspect of our lives, from banking through to dating, and onto our interactions with government. As more of our lives move online we face ever greater risks to our data and way of life from internet vulnerabilities and attacks. Web Security will examine the fundamentals behind common vulnerabilities and attacks, and will introduce students to ways of mitigating the risks associated with them. It will also examine some of the ethical challenges faced when evaluating security and disclosing vulnerabilities. INDICATIVE CONTENT The subject will examine some of the cyber security challenges faced during system implementation and deployment. In particular it will identity common attack vectors, covering in more detail some of the Open Web Application Security Project (OWASP) Top 10 list of web application vulnerabilities, which may include topics such as injection, cross‐site scripting, session hijacking, and cross‐site request forgery, amongst others. Where appropriate practical examples will be examined to relate theory to practice. The subject will discuss methods for mitigating the risks associated with such vulnerabilities, and may include discussions on distributed denial of service, input validation and sanitisation, penetration testing, and the associated ethical and legal constraints, automated vulnerability scanning, and web application firewalls. |
| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Applied High Performance Computing · 12.5 pts |
The use of physics-based computer simulation is a powerful tool in the scientific and engineering fields that allows for the investigation of phenomena that are often inaccessible by other means. As modern compute architectures continue to increase in terms of parallelism and power, so too can these simulations increase in scale and fidelity, but only when equipped with an understanding of the mathematics and underlying hardware, necessary to leverage this power. This subject will aim to develop such an understanding by tying together key tools and techniques used in the design of scientific software targeted at High Performance Computing (HPC) resources. This subject will introduce several numerical methods that are ubiquitous in the solution of ordinary differential equations (e.g. Euler and Runge-Kutta methods), partial differential equations (e.g. finite difference and finite element methods), linear systems (e.g. conjugate gradient method), and apply these tools to solve governing equations commonly found in areas such as fluid dynamics and thermodynamics. This subject will investigate the development of software targeting shared memory multicore architectures, distributed memory architectures with MPI, and GPU accelerators. |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Modelling Complex Software Systems · 12.5 pts |
AIMS |
Specialisation core
Students must complete 12.5 credit points:
| Accordion | |
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| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
Advanced specialisation selectives
Students must complete between 50 and 62.5 credit points:
| Accordion | |
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| Natural Language Processing · 12.5 pts |
AIMS Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| Advanced Algorithms and Data Structures · 12.5 pts |
Contemporary software systems such as search engines must deal with huge amounts of data, often in real time. In such cases, standard data structures and algorithms do not scale. This subject aims to provide an overview of contemporary advanced algorithms and data structures in computer science for such problems. These techniques serve as building blocks for solving complex algorithmic problems, and have many practical applications. |
| AI for Robotics · 12.5 pts |
AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including:
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| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| Computational Modelling and Simulation · 12.5 pts |
Computers are invaluable tools for modelling and simulating complex systems in a range of real-world domains. The complex behaviours exhibited by many biological, social, and technological systems - such as epidemics, urban systems, and robotics - challenge our ability to predict, analyse, and design such systems. Building computational models of these systems can help us better understand their structure and behaviour and make better decisions about their design and control. The aim of this subject is to provide students with a solid foundation in the conceptual and technical skills required to design, implement, and evaluate computational models of complex systems. INDICATIVE CONTENT Topics covered will be selected from:
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Program capstone
Students must complete 25 credit points:
| Accordion | |
|---|---|
| Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD. Enrolment in this subject requires a weighted average mark of 75 or above. Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability. By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found. |
| Software Project · 25 pts |
AIMS This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software. INDICATIVE CONTENT Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team. |
| Technology Innovation Project · 25 pts |
AIMSThis subject involves an in-depth investigation into a real-world problem, utilising a Design Thinking approach to identify technology-based solutions to the problem. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication. INDICATIVE CONTENTIndicative content includes design thinking methodology, customer & problem discovery, design ideation, development of innovative technology prototypes, and innovation presentations. |
Advanced CIS electives
Students can choose a maximum of 25 credit points:
| Accordion | |
|---|---|
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Models of Computation · 12.5 pts |
AIMS Formal logic and discrete mathematics provide the theoretical foundations for computer science. This subject uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.
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| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Sensor Networks and Applications · 12.5 pts |
AIMS Sensor networks are a key component of today’s increasingly pervasive computing technologies. In this subject, the aim is to develop an understanding of sensor network technologies from three different perspectives: sensing, communication, and computing (including hardware, software, and algorithms) and their applications. INDICATIVE CONTENT Topics covered include:
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| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Cluster and Cloud Computing · 12.5 pts |
AIMS The growing popularity of the Internet along with the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we do parallel and distributed computing (PDC). Cluster and Cloud Computing are two approaches for PDC. Clusters employ cost-effective commodity components for building powerful computers within local-area networks. Recently, “cloud computing” has emerged as the new paradigm for delivery of computing as services in a pay-as-you-go-model via the Internet. These approaches are used to tackle may research problems with particular focus on "big data" challenges that arise across a variety of domains. Some examples of scientific and industrial applications that use these computing platforms are: system simulations, weather forecasting, climate prediction, automobile modelling and design, high-energy physics, movie rendering, business intelligence, big data computing, and delivering various business and consumer applications on a pay-as-you-go basis. This subject will enable students to understand these technologies, their goals, characteristics, and limitations, and develop both middleware supporting them and scalable applications supported by these platforms. This subject is an elective subject in the Master of Information Technology. It can also be taken as an Advanced Elective subject in the Master of Engineering (Software). INDICATIVE CONTENT
Please view this video for further information: Cluster and Cloud Computing |
| Cryptography and Security · 12.5 pts |
AIMS The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions. This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology. INDICATIVE CONTENT The subject will be made up of three parts:
A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos. Topics drawn from:
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| Constraint Programming · 12.5 pts |
AIMS The aims for this subject is for students to develop an understanding of approaches to solving combinatorial optimization problems with computers, and to be able to demonstrate proficiency in modelling and solving programs using a high-level modelling language, and understanding of different solving technologies. The modelling language used is MiniZinc. INDICATIVE CONTENT Topics covered will include:
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| Declarative Programming · 12.5 pts |
AIMS Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques. INDICATIVE CONTENT
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| Advanced Database Systems · 12.5 pts |
AIMS Many applications require reliability in access to data, and data should not be lost even in the presence of hardware failures. The ability to retrieve and process the data very efficiently is also paramount even when multiple users access the data from remote sites simultaneously. With the increasing size of data used in these applications, advanced techniques for data management have emerged to make many such advanced requirements for access to data a reality. The subject covers the technologies used in advanced database systems that use these techniques. Topics covered will include: transactions, concurrency control, reliability, ACID properties, performance, indexing of both structured and unstructured data, query processing, and further topics on different database types and database architectures. INDICATIVE CONTENT Topics covered include:
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| Advanced Theoretical Computer Science · 12.5 pts |
AIMS At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing. INDICATIVE CONTENT
Example of assignment
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| Trustworthy Machine Learning · 12.5 pts |
AIMS As machine learning systems are increasingly integrated into critical and data sensitive applications, ensuring their confidentiality, reliability, robustness, and fairness becomes imperative. The complexity of modern AI models, coupled with evolving threats such as data inference, adversarial attacks, data poisoning, and biases, necessitates new methodologies to build and evaluate trustworthy machine learning systems. Trustworthy Machine Learning will explore techniques to enhance the privacy, security, interpretability, and safety in deployment of machine learning models, ensuring they operate reliably in real-world environments. INDICATIVE CONTENT The subject will begin by introducing the key dimensions of trustworthiness in machine learning, including privacy, robustness, reliability, and fairness. Students will examine real-world case studies that highlight failures and vulnerabilities in deployed AI systems. The first part of the subject will explore different types of information leakage that can arise under various threat models and examine methods for protecting sensitive data during analysis. The second part will introduce machine learning techniques that enhance model reliability, with a particular focus on unsupervised learning methods such as anomaly detection, alarm correlation, and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats. By the end of the subject, students will gain both theoretical knowledge and practical skills to design, evaluate, and deploy machine learning models with trustworthiness as a core principle. |
| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Machine Learning Applications for Health · 12.5 pts |
Artificial Intelligence (AI) is an ever growing field that holds the promise to revolutionise the way we develop drugs, treat and manage patients. In particular, Machine Learning has become an important tool to make sense of clinical data that is routinely collected and stored as electronic health records to enable personalised medicine. This subject aims to introduce students to different AI applications in health, using different clinical data sources and computational techniques, discussing their idiosyncrasies and the main challenges in the area. INDICATIVE CONTENT Topics covered may include: supervised, unsupervised learning and their applications in health scenarios, interpretable machine learning in health, natural language processing in health, process mining, data sources and clinical information modelling, data wrangling, harmonising and filtering, clinical image data and deep learning. |
| Text Analytics for Health · 12.5 pts |
AIMS Text analytics (also known as natural language processing) is becoming increasingly important in clinical and public health given the near-ubiquitous adoption of text-based Electronic Health Record (EHR) systems in clinical care, and the widespread use of social media and online communities for health-related peer support. This subject aims to provide students with a grounding in applied health-related text analytics using a range of different data sources and application areas. INDICATIVE CONTENT Topics covered may include: introduction to text analytics and text analytics for health applications; introduction to healthcare and public health; development of text analytics pipelines for clinical notes; best practice in data annotation; clinical information extraction using rule-based methods and machine learning; knowledge resources for clinical text analytics; clinical text analytics toolkits; utilization of text analytics and social media for health applications; sentiment and stance analysis in social media data; data management, privacy, and ethical issues in health-related text analytics. |
| Technopreneurship Project · 25 pts |
This subject provides students from the Master of Information Technology and Master of Information Systems programs with hands-on experience in deep innovation investigation and technopreneurship. Working in groups, students will engage in customer discovery, concept ideation, and iterative validated learning through prototype feature development and testing, leading towards startup preparation. It offers a unique opportunity to design and implement an innovative digital product while developing a strategy for bringing the innovation to market as an entrepreneur. |
| Large Data Methods & Applications · 12.5 pts |
This course provides an introduction to an important contemporary statistical toolset for applications including data science, machine learning, signal processing, financial engineering, biomedical engineering, communication systems and other high-dimensional statistical applications. The course will cover topics including introduction to random matrix theory models in engineering; eigenvalue distributions; finite-dimensional and large-dimensional techniques, covariance estimation, principal component analysis and spectral clustering. These topics will be supplemented by applications across a range of traditional and emerging domains involving big data sets. |
| Hardware Accelerated Computing · 12.5 pts |
Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include:
As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application |
| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Design Innovation and Leadership · 12.5 pts |
A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects. In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. |
| Information Visualisation · 12.5 pts |
Information visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (such as GEOM90008 Spatial Data Management), and the processing of data (such as GEOM90006 Spatial Data Analytics). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design. The subject will cover: Fundamentals of information visualisation and data graphics; visual thinking, human sensing and perception; foundations of data graphics and cartography; graphical user interface design; human computer interaction and human centred design. The lectures are also supported with several labs to develop student experience in this domain. In the labs, some tools such as Tableau and R libraries will be used to create a wide range of spatial and non-spatial visualisations in order to present data and discover patterns. These tools will also be used to create interactivity on visualisations. In addition, different visualisation types will be discussed and critiqued for different purposes. These will empower you to visualise various data sets in an appropriate form based on identified communication needs. |
| Spatial Data Management · 12.5 pts |
This subject combines practical spatial data management with the underpinning theories of spatial and spatiotemporal data representation and handling from Geographic Information Science. Spatial information is answering ‘where’ and ‘when’ questions – which are fundamental in decision making in complex systems, be it in urban planning, traffic and infrastructure management, environmental management, public health and sustainability, or any other social, economic, and environmental context. The subject introduces foundations of effective, efficient, and large-scale spatial data management. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data, in a modern ecosystem of spatial databases interfacing a geographic information system. The knowledge acquired is fundamental for subsequent studies in spatial data analytics and visualisation, and is of particular relevance to people wishing to establish a career in the spatial information, the environmental, or the planning industry. It is also suited for every postgraduate student who is looking for solid skills with Geographic Information Systems. In this subject, we will discuss the intricacies of computational representation and management of spatial information. The subject takes a spatial database perspective to management of extensive spatial datasets. The subject will cover the modelling, loading, transformation, analysis, and retrieval of spatial data in spatial databases. The subject covers data representations (vector, raster, and network data); spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees. The subject exposes the students to the whole lifecycle of spatial data management in a team-based project. Please view this video for further information: Spatial Data Management |
| Advanced Imaging · 12.5 pts |
This subject will introduce students to advanced imaging technologies and the methods for extracting quantitative information from multi-source imagery. This subject builds on the knowledge of subjects such as imaging the environment, by considering multi-source images of the target to provide additional information such as the distance from the target to object from which a three-dimensional representation can be constructed. It also considers imaging of targets where illumination is provided by the instrument rather than natural light reflection or radiation from the target. Students who successfully complete this subject may find work in a variety of remote sensing or specialist consultancies or agencies. The techniques learnt may also be applied to other industries such as quality control in manufacturing or recording of archaeological sites. The subject is of particular relevance to students wishing to establish a career in infrastructure engineering, civil engineering, property management, surveying, spatial information and urban planning but is also relevant to a range of disciplines where 3D building information should be considered. |
| Human-Computer Interaction · 12.5 pts |
How do you design interactive technologies that are useful, usable and satisfying? How can we better understand user needs in order to inform the design of new technologies? Introduction to Human-Computer Interaction addresses these questions, and students will learn about the key theories, concepts and industry methods that are crucial to the user-centered design process. Indicative Content
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| Digital Innovation & Technopreneurship · 12.5 pts |
AIMS In today’s digital world, the opportunities for innovation, and therefore entrepreneurs, are abundant. Hence, understanding the dynamic relationship between these two and how they interact to create commercially successful ventures has never been more critical. Innovation and entrepreneurship are complex topics widely debated in the business, education, and economics communities. This subject focuses on the nature of innovation in the rapidly evolving business landscape and considers what entrepreneurs need to create the climate for successful innovation. The subject is relevant to all students, whether they seek to be strategy consultants, budding entrepreneurs, or work as ‘intrapreneurs’ in large organisations. Students will discover in this subject that innovation is more than having great ideas and that entrepreneurs can emerge from diverse backgrounds and industries. The subject emphasises the proactive nature of innovation and ‘technopreneurship’, as it will focus on the role of technology, in driving digitally focused innovative entrepreneurial pursuits. Students will learn about the various behaviours, attitudes, values, and skills needed by the entrepreneur. By equipping students with the relevant knowledge and foundational professional skills, this subject aims to nurture an entrepreneurial mindset and inspire and prepare them for success in technology-driven industries by empowering them to create, build, sustain or advise innovative businesses in the digital era. INDICATIVE CONTENT The subject comprises four themes:
The subject involves advanced learning activities, including case-based, experiential, and team-based approaches. Students learn how to devise and pitch an innovation idea and then present it in written form as a startup planning report. They also develop the necessary professional skills, personal attributes and reflections to help them be successful on their entrepreneurial journey. |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Modelling Complex Software Systems · 12.5 pts |
AIMS |
Advanced specialisation core
Students must complete 12.5 credit points:
| Accordion | |
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| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
Advanced specialisation selectives
Students must complete between 50 and 62.5 credit points:
| Accordion | |
|---|---|
| Cryptography and Security · 12.5 pts |
AIMS The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions. This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology. INDICATIVE CONTENT The subject will be made up of three parts:
A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos. Topics drawn from:
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| Trustworthy Machine Learning · 12.5 pts |
AIMS As machine learning systems are increasingly integrated into critical and data sensitive applications, ensuring their confidentiality, reliability, robustness, and fairness becomes imperative. The complexity of modern AI models, coupled with evolving threats such as data inference, adversarial attacks, data poisoning, and biases, necessitates new methodologies to build and evaluate trustworthy machine learning systems. Trustworthy Machine Learning will explore techniques to enhance the privacy, security, interpretability, and safety in deployment of machine learning models, ensuring they operate reliably in real-world environments. INDICATIVE CONTENT The subject will begin by introducing the key dimensions of trustworthiness in machine learning, including privacy, robustness, reliability, and fairness. Students will examine real-world case studies that highlight failures and vulnerabilities in deployed AI systems. The first part of the subject will explore different types of information leakage that can arise under various threat models and examine methods for protecting sensitive data during analysis. The second part will introduce machine learning techniques that enhance model reliability, with a particular focus on unsupervised learning methods such as anomaly detection, alarm correlation, and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats. By the end of the subject, students will gain both theoretical knowledge and practical skills to design, evaluate, and deploy machine learning models with trustworthiness as a core principle. |
| Cyber Security Clinic · 12.5 pts |
This subject involves a mixture of classroom instruction and client-facing practice, in which students will work directly with not-for-profit and community organisations to help improve their cybersecurity practices and capabilities. Roughly the first half of the subject involves traditional lectures and tutorials in which students learn core cyber security principles and the conceptual frameworks for cyber security practice within organisations, with a focus on not-for-profit and community organisations and the unique threats that they face. The second half of the subject is primarily practical in nature, with students putting into practice the classroom knowledge by working directly with organisations to help improve their cyber security practice. Community and not-for-profit organisations are especially important because they are rich targets for cyber attacks yet often have limited resources to employ cyber security professionals or consultants. In the practical component of this subject, students will carry out tasks including asset inventory construction, cyber risk assessment, developing recommendations for and assessing the effectiveness of security controls, and developing cyber security training material. Students will work with client organisations under the supervision of a member of academic staff. The skills and knowledge students obtain, and the experience putting those into practice, will strengthen their employability. Indicative content covered in the classroom includes: Information security principles and threats overview; traditional information security controls and threat mitigations; ethics of information security practice; the threat landscape, with a focus on not-for-profit and community organisations; cybersecurity problem diagnosis; threat modelling and risk assessments; phishing and social engineering threats and controls; cyber security training and security behaviours; and misinformation and disinformation threats and mitigations. Entry to this subject requires permission from the subject coordinator. |
| High Integrity Systems Engineering · 12.5 pts |
AIMS High integrity systems are systems that must be engineered to a high level of dependability, that is, a high level of safety, security, reliability and performance. In this subject students will explore the aims, principles, techniques and tools that are used to analyse, design and implement dependable systems. INDICATIVE CONTENT Topics include: an introduction to high-integrity systems; safety critical systems and safety engineering; mathematical modelling of systems; fault tolerant systems design; design by contract; static verification; and model-based testing. |
| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Cybersecurity Practice in Organisations · 12.5 pts |
This subject presents a series of teaching cases to immerse students in the real-world context of cybersecurity in organisations. Through the teaching cases, students will confront challenges to the management practice of cybersecurity. The teaching cases will provoke robust student-led discussions that will disentangle complex cybersecurity management concepts and instill confidence and critical thinking in students and encourage them to express their own ideas. Teaching cases have been selected to address a wide range of management challenges and expose key risks that organisations face today. These include the risks of adopting a compliance culture, fragmentation in organisational structures, poor decision-making, inadequate skills and experience, and misperceptions about the role of the cybersecurity function. |
| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
Advanced CIS electives
Students can choose a maximum of 25 credit points:
| Accordion | |
|---|---|
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| AI Planning for Autonomy · 12.5 pts |
AIMS The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability. INDICATIVE CONTENT Topics are drawn from the field of advanced artificial intelligence including:
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| Advanced Theoretical Computer Science · 12.5 pts |
AIMS At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing. INDICATIVE CONTENT
Example of assignment
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| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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| Technopreneurship Project · 25 pts |
This subject provides students from the Master of Information Technology and Master of Information Systems programs with hands-on experience in deep innovation investigation and technopreneurship. Working in groups, students will engage in customer discovery, concept ideation, and iterative validated learning through prototype feature development and testing, leading towards startup preparation. It offers a unique opportunity to design and implement an innovative digital product while developing a strategy for bringing the innovation to market as an entrepreneur. |
| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Design Innovation and Leadership · 12.5 pts |
A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects. In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. |
| Human-Computer Interaction · 12.5 pts |
How do you design interactive technologies that are useful, usable and satisfying? How can we better understand user needs in order to inform the design of new technologies? Introduction to Human-Computer Interaction addresses these questions, and students will learn about the key theories, concepts and industry methods that are crucial to the user-centered design process. Indicative Content
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| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
Program capstone
Students must complete 25 credit points:
| Accordion | |
|---|---|
| Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD. Enrolment in this subject requires a weighted average mark of 75 or above. Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability. By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found. |
| Software Project · 25 pts |
AIMS This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software. INDICATIVE CONTENT Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team. |
| Technology Innovation Project · 25 pts |
AIMSThis subject involves an in-depth investigation into a real-world problem, utilising a Design Thinking approach to identify technology-based solutions to the problem. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication. INDICATIVE CONTENTIndicative content includes design thinking methodology, customer & problem discovery, design ideation, development of innovative technology prototypes, and innovation presentations. |
Advanced specialisation core
Students must complete 12.5 credit points:
| Accordion | |
|---|---|
| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
Advanced specialisation selectives
Students must complete between 50 and 62.5 credit points:
| Accordion | |
|---|---|
| Models of Computation · 12.5 pts |
AIMS Formal logic and discrete mathematics provide the theoretical foundations for computer science. This subject uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.
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| Sensor Networks and Applications · 12.5 pts |
AIMS Sensor networks are a key component of today’s increasingly pervasive computing technologies. In this subject, the aim is to develop an understanding of sensor network technologies from three different perspectives: sensing, communication, and computing (including hardware, software, and algorithms) and their applications. INDICATIVE CONTENT Topics covered include:
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| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Cluster and Cloud Computing · 12.5 pts |
AIMS The growing popularity of the Internet along with the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we do parallel and distributed computing (PDC). Cluster and Cloud Computing are two approaches for PDC. Clusters employ cost-effective commodity components for building powerful computers within local-area networks. Recently, “cloud computing” has emerged as the new paradigm for delivery of computing as services in a pay-as-you-go-model via the Internet. These approaches are used to tackle may research problems with particular focus on "big data" challenges that arise across a variety of domains. Some examples of scientific and industrial applications that use these computing platforms are: system simulations, weather forecasting, climate prediction, automobile modelling and design, high-energy physics, movie rendering, business intelligence, big data computing, and delivering various business and consumer applications on a pay-as-you-go basis. This subject will enable students to understand these technologies, their goals, characteristics, and limitations, and develop both middleware supporting them and scalable applications supported by these platforms. This subject is an elective subject in the Master of Information Technology. It can also be taken as an Advanced Elective subject in the Master of Engineering (Software). INDICATIVE CONTENT
Please view this video for further information: Cluster and Cloud Computing |
| Cryptography and Security · 12.5 pts |
AIMS The subject will explore foundational knowledge in the area of cryptography and information security. The overall aim is to gain an understanding of fundamental cryptographic concepts like encryption and signatures and use it to build and analyse security in computers, communications and networks. This subject covers fundamental concepts in information security on the basis of methods of modern cryptography, including encryption, signatures and hash functions. This subject is an elective subject in the Master of Engineering (Software). It can also be taken as an advanced elective in Master of Information Technology. INDICATIVE CONTENT The subject will be made up of three parts:
A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos. Topics drawn from:
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| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
| Stream Computing and Applications · 12.5 pts |
AIM With exponential growth in data generated from sensor data streams, search engines, spam filters, medical services, online analysis of financial data streams, and so forth, there is demand for fast monitoring and storage of huge amounts of data in real-time. Traditional technologies were not aimed to such fast streams of data. Usually they required data to be stored and indexed before it could be processed. Stream computing was created to tackle those problems that require processing and classification of continuous, high volume of data streams. It is highly used on applications such as Twitter, Facebook, High Frequency Trading and so forth. This subject will focus on the algorithms and data structures behind the analysis and management of streams. Theoretical underpinnings are emphasized, with implementation of some fundamental algorithms. INDICATIVE CONTENT
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| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Hardware Accelerated Computing · 12.5 pts |
Hardware acceleration for computationally intensive applications is of growing importance for improving workload performance in cloud data centres, the network edge, and IoT embedded devices. This subject introduces students to the basics of hardware design for field programmable gate arrays (FPGAs) which are widely used to accelerate algorithms in applications areas such as machine learning, artificial intelligence, networking, cryptography, and multimedia signal processing. In addition to covering FPGA fundamentals, the subject will take a systems-based approach to analysing algorithms for suitability of acceleration and mapping to heterogeneous computing resources. Topics covered in this subject may include:
As part of this subject, students will complete a significant design project in which they design, implement, verify, and benchmark a hardware accelerator for a selected application |
| Applied High Performance Computing · 12.5 pts |
The use of physics-based computer simulation is a powerful tool in the scientific and engineering fields that allows for the investigation of phenomena that are often inaccessible by other means. As modern compute architectures continue to increase in terms of parallelism and power, so too can these simulations increase in scale and fidelity, but only when equipped with an understanding of the mathematics and underlying hardware, necessary to leverage this power. This subject will aim to develop such an understanding by tying together key tools and techniques used in the design of scientific software targeted at High Performance Computing (HPC) resources. This subject will introduce several numerical methods that are ubiquitous in the solution of ordinary differential equations (e.g. Euler and Runge-Kutta methods), partial differential equations (e.g. finite difference and finite element methods), linear systems (e.g. conjugate gradient method), and apply these tools to solve governing equations commonly found in areas such as fluid dynamics and thermodynamics. This subject will investigate the development of software targeting shared memory multicore architectures, distributed memory architectures with MPI, and GPU accelerators. |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Modelling Complex Software Systems · 12.5 pts |
AIMS |
Advanced CIS electives
Students can choose a maximum of 12.5 credit points:
| Accordion | |
|---|---|
| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Natural Language Processing · 12.5 pts |
AIMS Much of the world's knowledge is stored in the form of text, and accordingly, understanding and harnessing knowledge from text are key challenges. In this subject, students will learn computational methods for working with text, in the form of natural language understanding, and language generation. Students will develop an understanding of the main algorithms used in natural language processing, for use in a diverse range of applications including machine translation, text mining, sentiment analysis, and question answering. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| Programming Language Implementation · 12.5 pts |
AIMS Good craftsmen know their tools, and compilers are amongst the most important tools that programmers use. There are many ways in which familiarity with compilers helps programmers. For example, knowledge of semantic analysis helps programmers understand error messages, and knowledge of code generation techniques helps programmers debug problems at assembly language level. The technologies used in compiler development are also useful when implementing other kinds of programs. The concepts and tools used in the analysis phases of a compiler are useful for any program whose input has a structure that is non-trivial to recognize, while those used in the synthesis phases are useful for any program that generates commands for another system. This subject provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles. INDICATIVE CONTENT The subject describes how compilers analyse source programs, how they translate them to target programs, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programs; code generation; and optimisation. |
| Constraint Programming · 12.5 pts |
AIMS The aims for this subject is for students to develop an understanding of approaches to solving combinatorial optimization problems with computers, and to be able to demonstrate proficiency in modelling and solving programs using a high-level modelling language, and understanding of different solving technologies. The modelling language used is MiniZinc. INDICATIVE CONTENT Topics covered will include:
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| Declarative Programming · 12.5 pts |
AIMS Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know. This subject presents declarative programming languages and techniques. INDICATIVE CONTENT
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| Advanced Database Systems · 12.5 pts |
AIMS Many applications require reliability in access to data, and data should not be lost even in the presence of hardware failures. The ability to retrieve and process the data very efficiently is also paramount even when multiple users access the data from remote sites simultaneously. With the increasing size of data used in these applications, advanced techniques for data management have emerged to make many such advanced requirements for access to data a reality. The subject covers the technologies used in advanced database systems that use these techniques. Topics covered will include: transactions, concurrency control, reliability, ACID properties, performance, indexing of both structured and unstructured data, query processing, and further topics on different database types and database architectures. INDICATIVE CONTENT Topics covered include:
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| Statistical Machine Learning · 12.5 pts |
AIMS With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks. This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. INDICATIVE CONTENT Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods. |
| Program Analysis and Transformation · 12.5 pts |
AIMS In the 1930s, Alan Turing and Konrad Zuse independently proposed designs of computing machines based on the idea that storage used for data and storage used for instructions be indistinguishable. This “stored-program” model formed the blueprint for all modern computers. The ability to treat programs as data turned out to be very powerful, as it meant that a program could be designed to read, generate, analyse and/or transform other programs, and even modify itself while running. This subject is concerned with meta-programs - programs that work on other programs, possibly generating programs as output. People routinely read, generate, analyse, test, and transform programs. For example, a programmer may look through code for potential buffer overruns, and may add runtime tests to avoid the security problems that could result. It is preferable, however, to automate such activity as far as we can, partly because that makes programmers more productive, and partly because computers generally are better at these tasks, avoiding human oversights and mistakes. This subject introduces the main techniques and applications of program analysis and transformation, including methods used by modern optimizing compilers and allied tools. INDICATIVE CONTENT
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| AI Planning for Autonomy · 12.5 pts |
AIMS The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals, in service of their users (the real world, human beings, or other agents). The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution, and the ethical impacts of agents with this ability. INDICATIVE CONTENT Topics are drawn from the field of advanced artificial intelligence including:
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| Advanced Theoretical Computer Science · 12.5 pts |
AIMS At the heart of theoretical computer science are questions of both philosophical and practical importance. What does it mean for a problem to be solvable by computer? What are the limits of computability? Which types of problems can be solved efficiently? What are our options in the face of intractability? This subject covers such questions in the content of a wide-ranging exploration of the nexus between logic, complexity and algorithms, and examines many important (and sometimes surprising) results about the nature of computing. INDICATIVE CONTENT
Example of assignment
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| Computational Modelling and Simulation · 12.5 pts |
Computers are invaluable tools for modelling and simulating complex systems in a range of real-world domains. The complex behaviours exhibited by many biological, social, and technological systems - such as epidemics, urban systems, and robotics - challenge our ability to predict, analyse, and design such systems. Building computational models of these systems can help us better understand their structure and behaviour and make better decisions about their design and control. The aim of this subject is to provide students with a solid foundation in the conceptual and technical skills required to design, implement, and evaluate computational models of complex systems. INDICATIVE CONTENT Topics covered will be selected from:
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| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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| Technopreneurship Project · 25 pts |
This subject provides students from the Master of Information Technology and Master of Information Systems programs with hands-on experience in deep innovation investigation and technopreneurship. Working in groups, students will engage in customer discovery, concept ideation, and iterative validated learning through prototype feature development and testing, leading towards startup preparation. It offers a unique opportunity to design and implement an innovative digital product while developing a strategy for bringing the innovation to market as an entrepreneur. |
| AI for Robotics · 12.5 pts |
AIMS: This subject focuses on the software and algorithms (i.e., artificial intelligence) that enable robotic systems to move autonomously through their environment and perform tasks. The key focus of this subject is the foundations of robotic systems that use software to move autonomously through their environment. This subject focus on the software & algorithms that enable the robot to perform tasks autonomously. Hence, this subject focused on artificial intelligence (AI) software & algorithms for robotics. The first main aim of the subject is to provide a foundation of the feedback loop that is core to all AI-enabled robots, namely: sensors measure the world around the robot; AI algorithms decide what action to take; the robot enacts that action by moving its joint or wheels; and the loop repeats endlessly. The second main aim of the subject is to provide implementation experience with cutting edge AI algorithm applicable to consumer and industrial robotics, where we consider both model-based method and reinforcement-learning methods. INDICATIVE CONTENT: Topics covered are at the intersection of automatic control and artificial intelligence, including:
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| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Design Innovation and Leadership · 12.5 pts |
A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects. In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. |
| Information Visualisation · 12.5 pts |
Information visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (such as GEOM90008 Spatial Data Management), and the processing of data (such as GEOM90006 Spatial Data Analytics). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design. The subject will cover: Fundamentals of information visualisation and data graphics; visual thinking, human sensing and perception; foundations of data graphics and cartography; graphical user interface design; human computer interaction and human centred design. The lectures are also supported with several labs to develop student experience in this domain. In the labs, some tools such as Tableau and R libraries will be used to create a wide range of spatial and non-spatial visualisations in order to present data and discover patterns. These tools will also be used to create interactivity on visualisations. In addition, different visualisation types will be discussed and critiqued for different purposes. These will empower you to visualise various data sets in an appropriate form based on identified communication needs. |
| Spatial Data Management · 12.5 pts |
This subject combines practical spatial data management with the underpinning theories of spatial and spatiotemporal data representation and handling from Geographic Information Science. Spatial information is answering ‘where’ and ‘when’ questions – which are fundamental in decision making in complex systems, be it in urban planning, traffic and infrastructure management, environmental management, public health and sustainability, or any other social, economic, and environmental context. The subject introduces foundations of effective, efficient, and large-scale spatial data management. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data, in a modern ecosystem of spatial databases interfacing a geographic information system. The knowledge acquired is fundamental for subsequent studies in spatial data analytics and visualisation, and is of particular relevance to people wishing to establish a career in the spatial information, the environmental, or the planning industry. It is also suited for every postgraduate student who is looking for solid skills with Geographic Information Systems. In this subject, we will discuss the intricacies of computational representation and management of spatial information. The subject takes a spatial database perspective to management of extensive spatial datasets. The subject will cover the modelling, loading, transformation, analysis, and retrieval of spatial data in spatial databases. The subject covers data representations (vector, raster, and network data); spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees. The subject exposes the students to the whole lifecycle of spatial data management in a team-based project. Please view this video for further information: Spatial Data Management |
| Human-Computer Interaction · 12.5 pts |
How do you design interactive technologies that are useful, usable and satisfying? How can we better understand user needs in order to inform the design of new technologies? Introduction to Human-Computer Interaction addresses these questions, and students will learn about the key theories, concepts and industry methods that are crucial to the user-centered design process. Indicative Content
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| Digital Innovation & Technopreneurship · 12.5 pts |
AIMS In today’s digital world, the opportunities for innovation, and therefore entrepreneurs, are abundant. Hence, understanding the dynamic relationship between these two and how they interact to create commercially successful ventures has never been more critical. Innovation and entrepreneurship are complex topics widely debated in the business, education, and economics communities. This subject focuses on the nature of innovation in the rapidly evolving business landscape and considers what entrepreneurs need to create the climate for successful innovation. The subject is relevant to all students, whether they seek to be strategy consultants, budding entrepreneurs, or work as ‘intrapreneurs’ in large organisations. Students will discover in this subject that innovation is more than having great ideas and that entrepreneurs can emerge from diverse backgrounds and industries. The subject emphasises the proactive nature of innovation and ‘technopreneurship’, as it will focus on the role of technology, in driving digitally focused innovative entrepreneurial pursuits. Students will learn about the various behaviours, attitudes, values, and skills needed by the entrepreneur. By equipping students with the relevant knowledge and foundational professional skills, this subject aims to nurture an entrepreneurial mindset and inspire and prepare them for success in technology-driven industries by empowering them to create, build, sustain or advise innovative businesses in the digital era. INDICATIVE CONTENT The subject comprises four themes:
The subject involves advanced learning activities, including case-based, experiential, and team-based approaches. Students learn how to devise and pitch an innovation idea and then present it in written form as a startup planning report. They also develop the necessary professional skills, personal attributes and reflections to help them be successful on their entrepreneurial journey. |
Program capstone
Students must complete 25 credit points:
| Accordion | |
|---|---|
| Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Computing. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. The subject is a research-based project, giving a capstone experience and piece of scholarship to students that is suitable as a pathway to PhD. Enrolment in this subject requires a weighted average mark of 75 or above. Completing enrolment into the subject will give students access, via the LMS, to information about possible topics, supervision, and timelines. Students should negotiate a project topic with a project supervisor before the start of semester. The topic must be relevant for the student’s specialisation, broadly interpreted. Students who are in doubt about the suitability of a chosen topic can contact the degree coordinator for an opinion about its suitability. By the end of Week 1 of semester, students must formally register their project, using an online form available via the LMS. If a chosen topic is deemed unsuitable, students will be alerted about this by the degree coordinator. Note that the degree coordinator's approval is an assessment hurdle requirement; if approval is not obtained, enrolment in the subject will be cancelled, until an acceptable project can be found. |
| Software Project · 25 pts |
AIMS This subject gives students in the Master of Information Technology experience in analysing, designing, implementing, managing and delivering a software project related to their stream of IT speciality. The aim of the subject is to guide students in being an independent member working within a team over the major phases of IT development, giving hands-on practical application of the topics seen throughout their degree. The subject also gives students a concrete understanding of teamwork processes and tools that underpin the practical aspects of developing software. INDICATIVE CONTENT Students will work in small teams to conceive, analyse, design, implement, test, and maintain a software product for a group of stakeholders. Workshops are tied closely to the projects and the particular phases of each project and will explore the application of theory to the project, including topics on: requirements analysis, software design, software release, communication, ethical principles, and software project management tools. Students will be required to demonstrate independence while working as part of a team. |
| Technology Innovation Project · 25 pts |
AIMSThis subject involves an in-depth investigation into a real-world problem, utilising a Design Thinking approach to identify technology-based solutions to the problem. Students working in groups will be required to perform research, customer and problem discovery, ideation, concept creation and validation, and technical implementation for a real-world challenge. The subject also provides students with skills and knowledge for improving written and oral communication. INDICATIVE CONTENTIndicative content includes design thinking methodology, customer & problem discovery, design ideation, development of innovative technology prototypes, and innovation presentations. |
Advanced specialisation core
Students must complete 12.5 credit points:
| Accordion | |
|---|---|
| Software Processes and Management · 12.5 pts |
AIMS The aim of this subject is to introduce students to the software engineering principles, processes, tools and techniques for analysing and managing complex software projects. INDICATIVE CONTENT Topics covered include: software engineering processes; project management; planning and scheduling; estimation and metrics; quality assurance; risk; configuration management; individuals and teams; ethics; change management; and project management tools. |
Advanced specialisation selectives
Students must complete between 50 and 62.5 credit points:
| Accordion | |
|---|---|
| Graphics and Interaction · 12.5 pts |
AIMS This subject introduces technologies and theoretical foundations of computer graphics and human-computer interaction (HCI) along with the aspects of human perception and action that inform their applications. The subject emphasises the 2D and 3D computer graphics pipeline, from the geometric modelling to visual representation and interaction with virtual environments. Core topics include geometry representation, 3D transformations, illumination models, rendering algorithms, animation, and object interactions. These technologies form the basis for developing 3D game engines and interactive applications across platforms ranging from PCs to tablet computers, incorporating natural user interfaces (NUIs). Applications explore computer games, virtual and augmented reality, movie visual effects, and social applications such as metaverse. The subject also extends into immersive multimodal interaction. This subject supports course-level objectives by allowing students to develop analytical and technical skills essential for developing and implementing real-world solutions in computer graphics and interaction applications. INDICATIVE CONTENT Topics are drawn from computer graphics and human-computer interaction including:
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| Mobile Computing Systems Programming · 12.5 pts |
AIMS Mobile devices are ubiquitous nowadays. Mobile computing encompasses technologies, devices and software that enable (wireless) access to services anyplace, anytime, and anywhere. This subject will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to the design, implementation, and evaluation of mobile computing. In particular, this subject will enable students to develop mobile phone applications that take advantage of the unique sensing capabilities of mobile devices, their multi-modal interaction capabilities, and their ability to sense and respond to context. |
| Advanced Interface Prototyping · 12.5 pts |
When designing for today’s devices, we still focus on smartphone and web applications. But what about the digital services and devices of tomorrow? This subject will explore design techniques and technologies for future technologies, which are increasingly moving away from traditional screen-based interaction. We will consider design strategies and implications for novel technology settings, such as interaction with artificial intelligence, virtual and augmented reality, smart homes, autonomous vehicles, and voice interfaces. Students will learn to extend their current design practices to cover off-screen interaction, while also learning modern prototyping techniques to envision future interactive technologies. This is a practical, project-based subject, in which students will follow an iterative design process for designing a new user experience. Lectures and workshops will offer practical tools, techniques, and processes for prototyping these experiences. |
| Human-AI Interaction · 12.5 pts |
This subject is designed to equip students with the essential knowledge and skills required to apply Artificial Intelligence (AI) techniques to the design, development, and evaluation of interactive technologies. The subject builds upon the foundational subjects in Human Computer Interaction (HCI) subjects, providing students with a deeper understanding of the role of AI in enhancing user experiences and designing intelligent interactive systems. Students will learn to apply AI algorithms and methodologies to enhance user experiences, usability, and interaction design, gaining the skills necessary to design and implement AI-driven HCI solutions that meet user needs and preferences through practical projects and hands-on exercises. |
| Social Computing · 12.5 pts |
Social Computing is a field of study that investigates computing techniques and systems to support, mediate, and understand aspects of social behaviours. Understanding the principles and foundations of Social Computing is important because of the rapid proliferation of social systems, particularly those aimed at end-users (e.g. social networking websites, crowd sourcing platforms, knowledge sharing platforms, etc.). This subject will introduce you to key concepts and principles of Social Computing, and provide you with training to investigate how these systems influence human behaviours, how to improve current implementations, and how to identify ways to better support social activities and interactions. |
| Digital Innovation & Technopreneurship · 12.5 pts |
AIMS In today’s digital world, the opportunities for innovation, and therefore entrepreneurs, are abundant. Hence, understanding the dynamic relationship between these two and how they interact to create commercially successful ventures has never been more critical. Innovation and entrepreneurship are complex topics widely debated in the business, education, and economics communities. This subject focuses on the nature of innovation in the rapidly evolving business landscape and considers what entrepreneurs need to create the climate for successful innovation. The subject is relevant to all students, whether they seek to be strategy consultants, budding entrepreneurs, or work as ‘intrapreneurs’ in large organisations. Students will discover in this subject that innovation is more than having great ideas and that entrepreneurs can emerge from diverse backgrounds and industries. The subject emphasises the proactive nature of innovation and ‘technopreneurship’, as it will focus on the role of technology, in driving digitally focused innovative entrepreneurial pursuits. Students will learn about the various behaviours, attitudes, values, and skills needed by the entrepreneur. By equipping students with the relevant knowledge and foundational professional skills, this subject aims to nurture an entrepreneurial mindset and inspire and prepare them for success in technology-driven industries by empowering them to create, build, sustain or advise innovative businesses in the digital era. INDICATIVE CONTENT The subject comprises four themes:
The subject involves advanced learning activities, including case-based, experiential, and team-based approaches. Students learn how to devise and pitch an innovation idea and then present it in written form as a startup planning report. They also develop the necessary professional skills, personal attributes and reflections to help them be successful on their entrepreneurial journey. |
Advanced CIS electives
Students can choose a maximum of 12.5 credit points:
| Accordion | |
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| Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an Australian setting. Working in small teams, students will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities constraints and recommendations of the exercise. Students will learn to: work with unstructured and incomplete information in Australian business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Global Business Practicum · 12.5 pts |
This subject provides an insight into the complexities and challenges of making business decisions in an international setting. Students will be assigned in small groups to research a business problem in an international context. Working in teams, they will conduct research, analyse, evaluate and propose practical solutions to an assigned business planning or business development exercise. This will be supported by online modules and seminar work equipping the students with knowledge of approaches, tools and techniques for completing the task and an understanding of report formats appropriate for conveying the results. During the practicum, in-depth research will be undertaken in identifying the scope, opportunities, constraints and recommendations of the exercise. Students will learn to work with unstructured and incomplete information in international business settings, to develop research and networks to support their enquiry, to work successfully in teams, to present their findings and seek and receive constructive feedback in a range of settings. Students will also be encouraged to plan, reflect and modify their approaches to improve the outcomes of their efforts in managing the business project. |
| Distributed Systems · 12.5 pts |
AIMS The subject aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed. INDICATIVE CONTENT Topics covered include: characterization of distributed systems, system models, interprocess communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, security, distributed file systems, and name services. |
| Introduction to Machine Learning · 12.5 pts |
AIMS Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis. INDICATIVE CONTENT Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning. |
| Quantum Software Fundamentals · 12.5 pts |
This subject will explore the fundamentals of quantum programming and quantum algorithm design. The subject will introduce students to a range of different quantum programming platforms and languages, and will include hands-on modules. The students will be prepared to write quantum programs, implement a range of simple quantum algorithms, such as Grover’s and Shor’s algorithms, and to execute quantum programs on a quantum computer through a cloud access. This subject will be made up of three parts:
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| Volunteer Experience in I.T. · 12.5 pts |
The purpose of this subject is to enable students who have completed voluntary external programming, systems/software or schools-based work during their course, that has not received credit elsewhere, to receive credit for this volunteer experience through the development of a retrospective and reflective portfolio of the experience and their work outcomes. This is to encourage students to take on volunteer work and for students to reflect on their experience, such as in open source projects or school volunteering. 2. Industry-based Work: Unpaid industry-based information technology/systems work that had a specific outcome 3. School-based Activity: Unpaid Engagement with a school and activities that are based in problem solving for programming or other technology-based engagement activities The nature of the activity is not prescribed but is assessed based on the volume of work and the outcomes of the project. Through use of a reflective portfolio, the student will provide evidence that typically includes: 2. Industry-based work: The student must demonstrate understanding of the industry processes and show how they have been involved in these processes 3. School-based activity: The student must demonstrate engagement with a school and activities that are based in problem solving activity for programming, programming itself, or other technology-based engagement activities |
| Computer Vision · 12.5 pts |
AIMS From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python. INDICATIVE CONTENT Topics covered may include:
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| The Ethics of Artificial Intelligence · 12.5 pts |
This subject aims to provide students with the necessary tools to: identify social and ethical issues of digital technology particularly artificial intelligence and reason about these issues; communicate concerns, or discuss ideas, from differing points of view; and ultimately build technology with awareness of, and respect for, inclusion and the responsibility that comes with building powerful tools. Not contemplating ethical or social implications of AI and other technological tools may open up unintended consequences and risks. Ethical dilemmas can also cause additional personal stress for individuals who lack the skills to think about them reflectively. For these reasons, the growing societal and ethical problems raised by artificial intelligence and other technologies have become a major focus of many organisations, including for start-ups, government, defence, and many corporations. Topics include:
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| Cryptocurrencies & decentralised ledgers · 12.5 pts |
AIMS: Cryptocurrencies enable the transfer of value entirely digitally between users, protected solely by cryptography. Modern cryptocurrencies, such as Bitcoin and Ethereum, rely on a public distributed ledger (also called a blockchain) to record who owns what. This subject introduces students to the theoretical foundations of cryptocurrencies from cryptography and distributed systems, as well as practical skills for programming applications which interact with decentralised ledgers. The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:
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| Internship · 25 pts |
AIMS This subject involves students undertaking professional work experience with a Host Organisation, generally at the Host Organisation’s premises. Students will work under the supervision of both an academic mentor and an external supervisor at the Host Organisation. By completing their internship as part of this subject, students will receive support in navigating their placement, guidance on maximising their learning from the experiences they gain and training in how to use these experiences when seeking employment. This subject uses structured reflection to help students develop the professional skills and competencies required by engineers and IT professionals. Each student is allocated an academic mentor to assist them in their development and support their well-being. Please view this video for further information: Internship |
| Information Visualisation · 12.5 pts |
Information visualisation is about using and designing effective mechanisms for presenting and exploring the patterns embedded in large and complex data sets, and to support decision making. Information Visualisation is important in a range of domains dealing with voluminous data rich in structure, among them, prominently, data in the spatial domain or data referenced to the spatial domain. Through its focus on presentation and interaction with spatial information, this subject complements related subjects that deal with the storage and querying of data (such as GEOM90008 Spatial Data Management), and the processing of data (such as GEOM90006 Spatial Data Analytics). This subject is vital for anyone wishing to work with large datasets. It will also be of relevance to those with an interest in design, especially graphical and interaction design. The subject will cover: Fundamentals of information visualisation and data graphics; visual thinking, human sensing and perception; foundations of data graphics and cartography; graphical user interface design; human computer interaction and human centred design. The lectures are also supported with several labs to develop student experience in this domain. In the labs, some tools such as Tableau and R libraries will be used to create a wide range of spatial and non-spatial visualisations in order to present data and discover patterns. These tools will also be used to create interactivity on visualisations. In addition, different visualisation types will be discussed and critiqued for different purposes. These will empower you to visualise various data sets in an appropriate form based on identified communication needs. |
| Spatial Data Management · 12.5 pts |
This subject combines practical spatial data management with the underpinning theories of spatial and spatiotemporal data representation and handling from Geographic Information Science. Spatial information is answering ‘where’ and ‘when’ questions – which are fundamental in decision making in complex systems, be it in urban planning, traffic and infrastructure management, environmental management, public health and sustainability, or any other social, economic, and environmental context. The subject introduces foundations of effective, efficient, and large-scale spatial data management. This subject will cover the concepts, methods, and approaches that allow for efficient representation, querying, and retrieval of spatial data, in a modern ecosystem of spatial databases interfacing a geographic information system. The knowledge acquired is fundamental for subsequent studies in spatial data analytics and visualisation, and is of particular relevance to people wishing to establish a career in the spatial information, the environmental, or the planning industry. It is also suited for every postgraduate student who is looking for solid skills with Geographic Information Systems. In this subject, we will discuss the intricacies of computational representation and management of spatial information. The subject takes a spatial database perspective to management of extensive spatial datasets. The subject will cover the modelling, loading, transformation, analysis, and retrieval of spatial data in spatial databases. The subject covers data representations (vector, raster, and network data); spatial operations, including geometric, topological, set-oriented, and network operations; spatial indexes and access methods, including quadtrees and R-trees. The subject exposes the students to the whole lifecycle of spatial data management in a team-based project. Please view this video for further information: Spatial Data Management |
| Knowledge Management Systems · 12.5 pts |
AIMS This subject focuses on how Knowledge Management (KM) and a range of Information Technologies and analysis techniques are used to support KM initiatives in organisations. Technologies likely to be considered are: collaborative and social media tools; corporate knowledge directories; data warehouses and other repositories of organizational memory; business intelligence including data-mining; process automation; workflow and document management. The emphasis is on high-level decision-making and the rationale of technology-based initiatives and their impact on organizational knowledge and its use. This subject supports course-level objectives by allowing students to develop analytical skills to understand the complexity of real-world KM work in organisations. It promotes innovative thinking around the deployment of existing and emerging information technologies for KM. The subject contributes to the development of independent critical inquiry, analysis and reflection. INDICATIVE CONTENT Techniques of analysis and design likely to be learned are: critical thinking, discourse analysis and design thinking. Real-world case studies in the form of fieldwork are conducted likely from the following domains: software industry; retail; creative/fashion industry; manufacturing; emergency management. Real case-study work will shape thinking about IT support for KM in these industries. |
| Digital Transformation of Health · 12.5 pts |
Healthcare is information intensive. Health data are generated, shared, consumed, and stored in a variety of partially overlapping complex networks. Healthcare lags behind many other sectors, despite efforts to use digital technologies to shape and improve health data and information processes since the middle of the 20th Century. The need for digital transformation of health is driven by socio-economic concerns (making healthcare more accessible and affordable) and patient safety (reducing medical errors, and redundant and ineffective interventions). This subject introduces the background, current state, and future opportunities of digital health. It provides a basic understanding of health and disease and how individuals experience both. It explores the nature of biomedical data, information, and knowledge - and how digital technologies are shaping the way these are used. Digital health technologies are examined from ethical, historical, technological, and psycho-social perspectives, considering positive and negative impacts. |
| Introduction to Quantum Computing · 12.5 pts |
This subject will introduce students to the world of quantum information technology, focusing on the fast developing area of quantum computing. The subject will cover basic principles of quantum logic operations in both digital and analogue approaches to quantum processors, through to quantum error correction and the implementation of quantum algorithms for real-world problems. In lab-based classes students will learn to use state-of-the-art quantum computer programing and simulation environments to complete a range of projects. |
| Human-Computer Interaction · 12.5 pts |
How do you design interactive technologies that are useful, usable and satisfying? How can we better understand user needs in order to inform the design of new technologies? Introduction to Human-Computer Interaction addresses these questions, and students will learn about the key theories, concepts and industry methods that are crucial to the user-centered design process. Indicative Content
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| Technopreneurship Project · 25 pts |
This subject provides students from the Master of Information Technology and Master of Information Systems programs with hands-on experience in deep innovation investigation and technopreneurship. Working in groups, students will engage in customer discovery, concept ideation, and iterative validated learning through prototype feature development and testing, leading towards startup preparation. It offers a unique opportunity to design and implement an innovative digital product while developing a strategy for bringing the innovation to market as an entrepreneur. |
| Design Innovation and Leadership · 12.5 pts |
A central innovation task is to identify the real problem that lies beneath the surface-level symptoms. Another is to find the best solution to that underlying problem. Professional work is often the same. Clearly defined tasks can frequently be delegated to a machine or a technician. Furthermore, because innovation problems are big and messy, we often need diverse teams to solve them. This subject aims to give you theoretical frameworks, practical insights, and preliminary skills to solve ambiguous problems and to work successfully in teams. You will develop these understandings, insights and skills by working on two projects. In the first, your multi-disciplinary team, supported by a mentor, will propose an innovation that helps a partner (industry, hospital, not-for-profit, start-up, the University) address a strategic challenge. Through that project, you will learn the “what and how” of delivering innovation-like projects – understanding the relationship between your challenge and the organisation’s strategy; designing, securing, and conducting interviews; analysing qualitative data to generate insights; ideation and creativity techniques to create value; stakeholder management; working in an intense team on an ambiguous problem; visual and oral communication. In the second, you will develop the ability to apply to the same concepts to yourself – How will you know what you want and need? How will you know if you need to change? How will you innovate yourself as your interests, needs, and work world shift? We aim for you and your team to own your project and your learning. Design Innovation and Leadership (DIAL) is delivered by the University's multi-award-winning Innovation Practice Program. To learn more about the Program, including a video about the subject, the range of organizations that have participated as sponsors, examples of past projects, and to hear students talk about their experiences in the predecessor subject, CIE/CIP, please go to the Innovation Practice Program’s website. All project sponsors will require that students maintain the confidentiality of their proprietary information. The University will require all students (except those working on projects sponsored by the University itself) to assign any Intellectual Property they create (other than Copyright in their Assessment Materials) to the sponsor of their project. The projects may vary in the hours needed for a successful outcome. Master of Engineering students please note: This subject has been integrated with the Skills Towards Employment Program (STEP) to create a straightforward pathway for completion of the Engineering Practice Hurdle (EPH). See the STEP page for more information. Please note: If you commenced a Master of Engineering degree prior to 2025, DIAL qualifies for the selective slot previously held by Creating Innovative Engineering. Engineering students who commenced in 2025 or later may only take DIAL as an elective. |
Program capstone
Students must complete 25 credit points:
| Accordion | |
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| HCI Research Project · 25 pts |
This subject involves in-depth investigation of a significant problem related to Human-Computer Interaction or a related discipline. The subject also provides students with skills and knowledge for analysing and solving problems, and enhanced written and oral communication skills. Under the supervision and guidance of an academic researcher, students are required to design and conduct a research investigation. This would typically involve a literature review, experimentation and data collection, and data analysis. The results will be reported as a thesis and in a public presentation. In some instances, it is expected that the results will also be submitted for publication in a conference or journal. |
| User Experience Design Project · 25 pts |
This subject gives students in the Human-Computer Interaction specialisation of the Master of Information Technology practical experience with the User Experience (UX) Design process. Students will take what they learned about fieldwork, prototyping, and evaluation, and apply it in a hands-on, industry-driven, UX project. You will be responsible for identifying and learning about potential stakeholders, exploring a range of design opportunities, and evaluating the success of your ideas. Along the way, you will produce relevant deliverables that support your project, communicate your ideas, and demonstrate your skills. At the end of the project, you will produce a portfolio of your work that you can use when applying for jobs and speaking to potential clients. From a brief, you will deconstruct and understand the scope of a client's idea. You will need to identify who their potential users are, how they currently solve similar problems, and what their expectations, challenges, and frustrations are. You will use data collection skills to capture the nuance of your different users and develop high-level requirements for the project. Next, you'll need to translate these requirements into initial sketches and subsequent high-fidelity prototypes (using industry standard tools). These prototypes will need to demonstrate how your ideas work and communicate the experience your users should expect. Finally, you will evaluate your prototypes, working with users to understand the opportunities and limitations of your design ideas. The deliverables you develop throughout this project, will form the foundations for your portfolio, where you concisely present your work, your learnings, and your reflections, to convince future employers that they should hire you. |