Master of Computer Science

AIMS

Research is a process of acquiring new knowledge by systematically and rigorously applying methods to address well-formulated questions. To be valuable, new knowledge must address a significant theoretical question, it must be supported by evidence and be able to stand up to critical scrutiny, and its presentation to other researchers and/or to the public must be persuasive. This subject is an introduction to research thinking, skills and methodologies as they apply to computing and related disciplines. The subject will foster the development of critical thinking, a sceptical and rigorous approach, and awareness of research ethics. This subject will be particularly useful for students contemplating undertaking a research degree, or for students currently enrolled in a research degree (MPhil or PhD) or a course-work degree with a research project (MIT, MIS).

INDICATIVE CONTENT

Research skills covered will include: surveying relevant literature, developing productive research questions, selecting and designing appropriate methods, analysing data and reasoning about their theoretical implications, communicating research both in writing and through oral presentation, and understanding the ethics of research. Qualitative methods covered include: ethnography, field data collection techniques (interviews, focus groups), thematic analysis, case studies and design-based research. Quantitative methods covered include: statistical thinking and techniques, hypothesis testing, experiment design, survey design, simulation studies.

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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.

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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

• The dangers of destructive update
• Functional programming
• Recursion
• Strong type systems
• Parametric polymorphism
• Algebraic types
• Type classes
• Defensive programming practice
• Higher order programming
• Currying and partial application
• Lazy evaluation
• Monads
• Logic programming
• Unification and resolution
• Nondeterminism, search, and backtracking

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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.

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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.

The programming language used in this subject is Python. No lectures or workshops on Python will be delivered.

INDICATIVE CONTENT

Topics are drawn from the field of advanced artificial intelligence including:

• Search algorithms and heuristic functions
• Classical (AI) planning
• Markov Decision Processes
• Reinforcement learning
• Game theory
• Ethics in AI planning

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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

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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.

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Technological advances in DNA sequencing, RNA sequencing and proteomics have provided a wealth of data from which biological insight can be obtained. Refining this data is a non-trivial matter due to the increased input sizes seen in modern high-throughput bioinformatics. This subject provides algorithmic strategies and data structures capable of meeting the challenge. While focused on bioinformatic data, the concepts herein apply to big data analysis as a whole.

This subject covers key algorithms and data structures used in bioinformatics and assumes you have experience in programming. Strategies which frequently appear in modern software are explored so that bioinformatics tools may be appropriately selected, executed, and interpreted. This exploration yields a toolkit from which new computational methods can be created. Indicative topics include sequence operations for comparison, alignment and indexing, graph data structures in the context of genome assembly, phylogenetics and network analysis, and both supervised and unsupervised machine learning within the fields of optimisation, dimensionality reduction, clustering and classification.

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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

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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.

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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:

• Synchronous and asynchronous network algorithms that address resource allocation, communication
• Consensus among distributed processes
• Distributed data structures
• Data consistency
• Deadlock detection
• Lader election, and
• Global snapshots issues.

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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

• Cluster computing: elements of parallel and distributed computing, cluster systems architecture, resource management and scheduling, single system image, parallel programming paradigms, cluster programming with MPI
• Utility computing: foundations and grid computing technologies
• Cloud computing: cloud platforms, Virtualization, Cloud Application Programming Models (Task, Thread, and MapReduce), Cloud applications, and future directions in utility and cloud computing
• "Big data" processing and analytics in distributed environments

Please view this video for further information: Cluster and Cloud Computing

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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.

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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:

• Text classification and unsupervised topic discovery
• Vector space models for natural language semantics
• Structured prediction for tagging
• Syntax models for parsing of sentences and documents
• N-gram language modelling
• Automatic translation, and multilingual methods
• Relation extraction and coreference resolution

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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:

• Cryptography: the essentials of public and private key cryptography, stream ciphers, digital signatures and cryptographic hash functions
• Access Control: the essential elements of authentication and authorization; and
• Secure Protocols; which are obtained through cryptographic techniques.

A particular emphasis will be placed on real-life protocols such as Secure Socket Layer (SSL) and Kerberos.

Topics drawn from:

• Symmetric key crypto systems
• Public key cryptosystems
• Hash functions
• Authentication
• Secret sharing
• Protocols
• Key Management.

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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.

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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:

• Introduction to High Performance Database Systems
• Issues of Performance and Reliability
• Transaction Processing
• Recovery from Failures
• Map Reduce Models

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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.

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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.

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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.

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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.

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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:

• the use of models for science, engineering, and policy
• dynamical systems analysis
• complexity and emergent behaviour
• agent-based models
• design, communication, and evaluation of models
• analysis and visualisation of model behaviour
• case study exemplars of specific types of models, such as:

• • spatial models (e.g., transportation)
  • network models (e.g., epidemics)
  • adaptive models (e.g., robotics)

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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:

1. Fundamentals of quantum computing and quantum programming, including running quantum programs on actual cloud-based quantum computers.
2. Programming fundamental quantum algorithms, such as the Deutsch–Jozsa, Grover, Shor and HHL algorithms.
3. Quantum programming for cutting edge research topics, such as quantum error correction, variational quantum circuits and quantum machine learning.

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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:

• Basics of image formation
• Illumination and reflectance models
• Colour spaces
• Feature detectors and descriptors
• Stereo correspondence
• Methods for recovering three-dimensional shape
• Image segmentation
• Categorical and instance-level recognition

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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:

• the history of artificial intelligence
• established ethical theories and concepts and their relation to artificial intelligence and technology
• fairness, equity, and discrimination in automated decision making
• accountability, explainability, and transparency of AI
• practical approaches and ethical frameworks for designing, developing and deploying technology responsibly

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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.

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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.

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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.

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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.

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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.

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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.

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AIMS

Mathematical modelling is important for understanding and engineering many facets of complex systems. The aim of this subject is for students to understand the range and use of mathematical theories and notations in the analysis of discrete systems, how to abstract the key aspects of a problem into a model to handle complexity, and how models can be employed to verify large-scale complex software systems.

INDICATIVE CONTENT

Topics covered will be selected from: deterministic and stochastic modelling; dynamical systems; cellular automata; agent-based modelling; complex networks; simulation and analysis of complex systems; concurrent systems modelling, analysis and implementation; process algebra; temporal logic and model checking.

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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.

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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.

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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:

• Cyber-physical feedback system formulation, such as: black-box and grey-box modelling, stability and robustness safety requirements, hierarchical and network control architectures.
• Safety and convergence guarantees for model-based methods, such as: learning models from data; adaptive control schemes; stability and robustness of PID and MPC control approaches.
• Connections between optimal control and reinforcement learning formulations for robotics.
• Reinforcement learning for robotics, such as: actor-critic methods, on-policy versus off-policy learning, sample efficiency, transferring simulation-based learning to real-world robots

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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.

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Much of the world’s data relates to processes and objects situated in space. Spatial data is a rich source of insights about patterns, processes, trends and behaviours in space and time. To tap into these insights, specialised statistical, analytical and computational techniques are required.

This subject exposes students to fundamental aspects of spatial analytics. Students are introduced to key techniques and principles for the analysis of point, area, and field data, covering concepts such as point pattern analysis, spatial autocorrelation and geostatistics. As part of putting these techniques and principles into practice, students learn computational thinking approaches and acquire technical software skills in a high-level scripting language (such as Python and R) that enable them to effectively address spatial data science problems across a variety of domains.

The subject partners with other subjects on spatial data management and visualisation and is of particular relevance to people wishing to establish a career in digital infrastructure, spatial information technology, or the quantitative environmental modelling or planning sectors.

The subject delivers underlying and cross-disciplinary concepts of geographic information science (GIS) and spatial analytics in managing environmental and infrastructure data, and the visual representation of spatial and temporal information. Relating these relevant concepts to applications through case study examples from various sectors such as digital infrastructure, spatial information technology, quantitative environmental modelling, urban sustainability, and planning. Defining and realizing a student-driven project employing a modern scripting language and spatial-temporal relationships of the observed data from real world.

Students will be provided with pointers and material to familiarise themselves with the tools used in this subject before the semester starts; this element of preparation is expected for successful participation in the subject. Advice will be provided on LMS.

Please view this video for further information: Spatial Data Analytics

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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.

View detailed information in the Handbook

Students undertake a year-long (full-time equivalent) research project under the supervision of academic staff from the School of Computing and Information Systems.

For a full-time enrolment, the subject continues over two consecutive study periods (full-time) with students enrolling in parts 1 and 2 in one study period, and then parts 3 and 4 in the consecutive study period, for a combined total enrolment of 100 credit points. To enable part-time study, part-time students may take one subject in a single semester. A mark for the subject/s will not be awarded until the entire 100 points of enrolment has been completed. All subjects are offered in both semester 1 and 2.

Satisfactory completion of the research proposal (in parts 1 and 2) is required to progress to parts 3 and 4.

Information provided on this page applies to all 'parts' of the subject:

• Computer Science Research Project Pt 1 (25 pts)
• Computer Science Research Project Pt 2 (25 pts)
• Computer Science Research Project Pt 3 (25 pts)
• Computer Science Research Project Pt 4 (25 pts)

View detailed information in the Handbook

Students undertake a year-long (full-time equivalent) research project under the supervision of academic staff from the School of Computing and Information Systems.

For a full-time enrollment, the subject continues over two consecutive study periods (full-time) with students enrolling in parts 1 and 2 in one study period, and then parts 3 and 4 in the consecutive study period, for a combined total enrollment of 100 credit points. To enable part-time study, part-time students may take one subject in a single semester. A mark for the subject/s will not be awarded until the entire 100 points of enrollment has been completed. All subjects are offered in both semester 1 and 2.

Satisfactory completion of the research proposal (in parts 1 and 2) are required to progress to parts 3 and 4.

For full information about this subject, please refer to the Handbook page for Part 1 of the project:

Computer Science Research Project Pt 1 (25 pts)

View detailed information in the Handbook

Students undertake a year-long (full-time equivalent) research project under the supervision of academic staff from the School of Computing and Information Systems.

For a full-time enrollment, the subject continues over two consecutive study periods (full-time) with students enrolling in parts 1 and 2 in one study period, and then parts 3 and 4 in the consecutive study period, for a combined total enrollment of 100 credit points. To enable part-time study, part-time students may take one subject in a single semester. A mark for the subject/s will not be awarded until the entire 100 points of enrollment has been completed. All subjects are offered in both semester 1 and 2.

Satisfactory completion of the research proposal (in parts 1 and 2) are required to progress to parts 3 and 4.

For full information about this subject, please refer to the Handbook page for Part 1 of the project:

Computer Science Research Project Pt 1 (25 pts)

View detailed information in the Handbook

Students undertake a year-long (full-time equivalent) research project under the supervision of academic staff from the School of Computing and Information Systems.

For a full-time enrollment, the subject continues over two consecutive study periods (full-time) with students enrolling in parts 1 and 2 in one study period, and then parts 3 and 4 in the consecutive study period, for a combined total enrollment of 100 credit points. To enable part-time study, part-time students may take one subject in a single semester. A mark for the subject/s will not be awarded until the entire 100 points of enrollment has been completed. All subjects are offered in both semester 1 and 2.

Satisfactory completion of the research proposal (in parts 1 and 2) are required to progress to parts 3 and 4.

For full information about this subject, please refer to the Handbook page for Part 1 of the project:

Computer Science Research Project Pt 1 (25 pts)

View detailed information in the Handbook