Master of Data Science

Statistical models are central to data science applications. Modelling approaches such as linear and generalized linear models, mixed models, and non-parametric regression are developed. Applications to time series, longitudinal, and spatial data are discussed. Methods for causal inference and handling missing data are introduced.

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Modern statistics and data science deals with data having multiple dimensions. Multivariate methods are used to handle these types of data. Approaches to supervised and unsupervised learning with multivariate data are discussed. In particular, methods for classification, clustering, and dimension reduction are introduced, which are particularly suited to high-dimensional data. Both parametric and nonparametric approaches are discussed.

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Computing techniques and data mining methods are indispensable in modern statistical research and data science applications, where "Big Data" problems are often involved. This subject will introduce a number of recently developed methods and applications in computational statistics and data science that are scalable to large datasets and high-performance computing. The data mining methods to be introduced include general model diagnostic and assessment techniques, kernel and local polynomial nonparametric regression, basis expansion and nonparametric spline regression, and generalised additive models. Important statistical computing algorithms and techniques used in data science will be explained in detail. These include unsupervised learning of meaningful components, bootstrap resampling and inference, cross-validation, the Expectation-Maximisation (EM) algorithm and variational approximation, and Markov chain Monte Carlo methods including adaptive rejection and squeeze sampling, sequential importance sampling, slice sampling, Gibbs samplers and the Metropolis--Hastings algorithm.

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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

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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This subject introduces probability and the theory underlying modern statistical inference. Properties of probability are reviewed, univariate and multivariate random variables are introduced, and their properties are developed. It demonstrates that many commonly used statistical procedures arise as applications of a common theory. Both classical and Bayesian statistical methods are developed. Basic statistical concepts including maximum likelihood, sufficiency, unbiased estimation, confidence intervals, hypothesis testing and significance levels are discussed. Computer packages are used for numerical and theoretical calculations.

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Supervised statistical learning is based on the widely used linear models that model a response as a linear combination of explanatory variables. Initially this subject develops an elegant unified theory for a quantitative response that includes the estimation of model parameters, hypothesis testing using analysis of variance, model selection, diagnostics on model assumptions, and prediction. Some classification methods for qualitative responses are then developed. This subject then considers computational techniques, including the EM algorithm. Bayes methods and Monte-Carlo methods are considered. The subject concludes by considering some unsupervised learning techniques.

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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:

• Java basics
• Console input/output
• Control flow
• Defining classes
• Using object references
• Programming with arrays
• Inheritance
• Polymorphism and abstract classes
• Exception handling
• UML basics
• Interfaces
• Collection & Generics
• Advanced Topics

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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.

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AIMS

Data processing is fundamental to computing and data science. This subject covers various aspects of data processing including database management, representation and analysis of data, information retrieval, visualisation and reporting, and cloud computing. This subject includes an emphasis on both tools and underlying foundations.

INDICATIVE CONTENT

The subject's focus is on the data pipeline, and activities known colloquially as 'data wrangling'. Indicative topics covered include:

• Capturing data (data ingress)
• Data representation and storage
• Cleaning, normalisation and filling in missing data (imputation)
• Combing multiple sources of data (data integration)
• Query languages and processing
• Scripting to support the data pipeline
• Visualisation and presentation

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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).

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This capstone project will provide the culmination of the Master of Data Science degree. It will apply the skills developed during the degree to a practical problem of relevance to science, industry, commerce or society in general. Students will work in teams under only general guidance from staff members. Students will complete diaries to log their work on the project so that the extent of their contribution to group projects can be determined. In the first part of the project students will complete a literature review and a plan for their project.

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This capstone project will provide the culmination of the Master of Data Science degree. It will apply the skills developed during the degree to a practical problem of relevance to science, industry, commerce or society in general. Students will continue to work in their teams established in MAST90106 Data Science Project Part 1, again under only general guidance from staff members. They are expected to present technically correct results in a fashion acceptable to industry-based and other clients.
Students will be continue to be expected to complete diaries to log their work on the project so that the extent of their contribution to group projects can be determined. In this part of the project students will complete the project and present a group project report and oral presentation.

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In this subject, students undertake a substantial research program in the area of Data Science. The research will be conducted under the supervision of a member of the School of Mathematics and Statistics or the Computing and Information Systems academic staff. The results will be reported in the form of a thesis and an oral presentation.

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In this subject, students undertake a substantial research program in the area of Data Science. The research will be conducted under the supervision of a member of the School of Mathematics and Statistics or the Computing and Information Systems academic staff. The results will be reported in the form of a thesis and an oral presentation.

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Modern data collection technologies are creating unprecedented challenges in statistics and data science related to the analysis and interpretation of massive data sets where observations exhibit patterns through time and space. This subject introduces probability models and advanced statistical inference methods for the analysis of temporal and spatio-temporal data. The subject balances rigorous theoretical development of the methods and their properties with real-data applications. Topics include inference methods for univariate and multivariate time series models, spatial models, lattice models, and inference methods for spatio-temporal processes. The subject will also address aspects related to computational and statistical trade-offs, and the use of statistical software.

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Bayesian inference treats all unknowns as random variables, and the core task is to update the probability distribution for each unknown as new data is observed. After introducing Bayes’ Theorem to transform prior probabilities into posterior probabilities, the first part of this subject introduces theory and methodological aspects underlying Bayesian statistical learning including credible regions, prior choice, comparisons of means and proportions, multi-model inference and model selection. The second part of the subject will cover practical implementations of Bayesian methods through Markov Chain Monte Carlo computing and real data applications, focusing on (generalised) linear models and concluding by exploring machine learning techniques such as Gaussian processes.

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The use of mathematical optimisation is widespread in business, where it is a key analytical tool for managing and planning business operations. It is also required in many industrial processes and is useful to government and community organizations. This subject will expose students to operations research techniques as used in industry. A heavy emphasis will be placed on the modelling process that turns an industrial problem into a mathematical formulation. The focus will then be on how to solve the resulting mathematical problem with mixed-integer programming techniques.

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The subject covers some key aspects of the theory of stochastic processes that plays a central role in modern probability and has numerous applications in natural sciences and industry. We discuss the following topics: ways to construct and specify random processes, functional central limit theorem, Levy processes, renewal processes and Markov processes (discrete and continuous state space). Applications to modelling random phenomena evolving in time are discussed throughout the course.

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This subject builds on methods and techniques learned in theoretical subjects by studying the application of statistics in real contexts. Emphasis is on the skills needed for a practising statistician, including the development of mature statistical thinking, organizing the structure of a statistical problem, the contribution to the design of research from a statistical point of view, measurement issues and data processing. The subject deals with thinking about data in a broad context, and skills required in statistical consulting.

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Mathematical modelling of various types of risk has become an important component of the modern financial industry. The subject discusses the key aspects of the mathematics of market risk. Main concepts include loss distributions, risk and dependence measures, copulas, risk aggregation and allocation principles, elements of extreme value theory. The main theme is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers.

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This subject provides an introduction to stochastic calculus and mathematics of financial derivatives. Stochastic calculus is essentially a theory of integration of a stochastic process with respect to another stochastic process, created for situations where conventional integration will not be possible. Apart from being an interesting and deep mathematical theory, stochastic calculus has been used with great success in numerous application areas, from engineering and control theory to mathematical biology, theory of cognition and financial mathematics.

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This subject explores a range of key concepts in modern Probability Theory that are fundamental for Mathematical Statistics and are widely used in other applications. We study measurable space, product measure, Fubini's theorem, conditional expectation and conditional probability, construction of i.i.d. and beyond, discrete-time martingales.

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The theory of statistical inference is important for applied statistics and as a discipline in its own right. After reviewing random samples and related probability techniques including inequalities and convergence concepts the theory of statistical inference is developed. The principles of data reduction are discussed and related to model development. Methods of finding estimators are given, with an emphasis on multi-parameter models, along with the theory of hypothesis testing and interval estimation. Both finite and large sample properties of estimators are considered. Applications may include robust and distribution free methods, quasi-likelihood and generalized estimating equations. It is expected that students completing this course will have the tools to be able to develop inference procedures in novel settings.

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Random matrix theory is a diverse mathematical tool. It draws together ideas from linear algebra, multivariate calculus, analysis, probability theory, group and representation theory, differential geometry, combinatorics and mathematical physics. It also enjoys a wide number of applications, ranging from wireless communication in engineering, to time series analysis in statistics, quantum chaos and quantum field theory in physics, to the Riemann zeta function zeros and prime numbers in number theory. A self contained development of random matrix theory will be undertaken in this course from various viewpoints.

Topics to be covered include:

• Gaussian random matrix models and their application in likelihood analysis and modelling covariance matrices in time series analysis;
• eigenvalue densities and the concept of eigenvalue repulsion;
• classification of random matrices ensembles;
• derivation of Jacobians for matrix transformations such as diagonalisations;
• joint eigenvalue densities and correlation functions;
• orthogonal polynomials and the concept of determinantal point processes;
• supersymmetry and non-linear sigma-models;
• the log-gas picture;
• free probability theory and its application to matrix sums and products.

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Complex data consisting of dependent measurements collected at different times and locations are increasingly important in a wide range of disciplines, including environmental sciences, biomedical sciences, engineering and economics. This subject will introduce you to advanced statistical methods and probability models that have been developed to address complex data structures, such as functional data, geo-statistical data, lattice data, and point process data. A unifying theme of this subject will be the development of inference, classification and prediction methods able to cope with the dependencies that often arise in these data.

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This subject develops the advanced topics and methods of stochastic processes and discusses possible applications of the models covered in the course. It serves to prepare students for research in Probability Theory. The specific content will vary depending on the subject coordinator.

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Modern data collection technologies are creating unprecedented challenges in statistics and data science related to the analysis and interpretation of massive data sets where observations exhibit patterns through time and space. This subject introduces probability models and advanced statistical inference methods for the analysis of temporal and spatio-temporal data. The subject balances rigorous theoretical development of the methods and their properties with real-data applications. Topics include inference methods for univariate and multivariate time series models, spatial models, lattice models, and inference methods for spatio-temporal processes. The subject will also address aspects related to computational and statistical trade-offs, and the use of statistical software.

View detailed information in the Handbook

Bayesian inference treats all unknowns as random variables, and the core task is to update the probability distribution for each unknown as new data is observed. After introducing Bayes’ Theorem to transform prior probabilities into posterior probabilities, the first part of this subject introduces theory and methodological aspects underlying Bayesian statistical learning including credible regions, prior choice, comparisons of means and proportions, multi-model inference and model selection. The second part of the subject will cover practical implementations of Bayesian methods through Markov Chain Monte Carlo computing and real data applications, focusing on (generalised) linear models and concluding by exploring machine learning techniques such as Gaussian processes.

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Stochastic optimisation encompasses many diverse areas including control theory, reinforcement learning, multiarmed bandit problems, simulation optimisation, and neural networks. Stochastic optimisation can be succinctly described as sequential decision making under uncertainty. In a sequential decision problem, the system being modelled progresses through a finite or infinite number of stages. At each stage, the system is in a particular state taken from a discrete or continuous state space, and decision (action) is taken which may depend on the stage and/or state. The aim is to design a set of decisions or actions (a policy) at each stage, so that an objective function is optimised. Randomness is incorporated into the problem by exogeneous information that is only realised once a decision is made at each stage. Topics include Markov decision processes, approximate dynamic programming, reinforcement learning, simulation optimisation, and robust optimisation. This subject provides a rigorous mathematical treatment of stochastic optimisation, and will include applications selected from logistics, finance, transportation, health, resource allocation, e-commerce, and supply chain management.

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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

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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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.

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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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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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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 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

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:

• Modelling with Constraints
• Global constraints
• Multiple Modelling
• Model Debugging
• Scheduling and Packing
• Finite domain constraint solving
• Mixed Integer Programming

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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

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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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

• Why stream processing is important
• Hash functions, probability, and fundamental data structures
• Data stream model
• Data stream algorithms: Sampling, sketching, distinct items, frequent items, frequency moments, etc.
• Data stream mining: clustering, histograms, query tracking
• Graph streams: connectivity, matchings, covers

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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

• Turing machines
• The Church-Turing Thesis
• Decidable languages
• Reducability
• Time Complexity: The classes P and NP, NP-complete problems
• Space complexity: including sub-linear space
• Circuit complexity
• Approximation algorithms
• Probabilistic complexity classes
• Additional topics may include descriptive complexity, interactive proofs, communication complexity, complexity as applied to cryptography
• Space complexity, including sub-linear space
• Finite state automata, pushdown automata, regular languages, context-free languages to the Recommended Background Knowledge.

Example of assignment

• Proving the equivalence of a variant of a standard machine to the original version
• Describing an NP-hardness reduction
• Designing an approximation algorithm for an NP-hard problem.

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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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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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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.

INDICATIVE CONTENT:

The subject will be composed of core topics from cryptocurrencies and distributed lectures and will be drawn from a list including:

• Digital signatures
• Authenticated data structures
• Zero-knowledge proofs
• Decentralised consensus protocols
• Smart contract programming.

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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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This subject involves completion of an 80-100 hour science or technology work placement integrating academic learning in science areas of study, employability skills and attributes and an improved knowledge of science and technology organisations, workplace culture and career pathways. The placement is supplemented by pre- and post-placement classes designed to develop an understanding of science and technology professions, introduce skills for developing, identifying and articulating employability skills and attributes and linking them to employer requirements in the science and technology domains. Work conducted during the placement will be suitable for a graduate level of expertise and experience. While immersed in a work environment, students will be expected to challenge themselves by accepting roles and responsibilities that stretch their existing capabilities. They will interrogate the requirements of specific careers and continually monitor their own progress towards developing the necessary knowledge, skills and attributes to thrive in these roles.

Students will be responsible for identifying a suitable work placement prior to the semester. Application for credit need to be submitted via the Internships Portal at least 3 weeks prior to internship commencement and within the Key Dates mentioned on the website. More information is available on the subject webpage here: https://science.unimelb.edu.au/students/plan-your-study/internship-subjects. If you have questions on how and where to find internship, you should contact the Careers and Industry team in the Faculty of Science at hyperlink: https://forms.your.unimelb.edu.au/4747166?SID=a3xOY000000018z

On completion of the subject, students will have completed and reported on a course-related project in a science or technology workplace. They will also have enhanced employability skills including communication, interpersonal, analytical and problem-solving, organisational and time-management, and an understanding of career planning and professional development.

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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.

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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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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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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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Being an effective communicator is essential to gaining employment and for ongoing career success. Technical skills matter, but to be a valued member of a workplace, you need to be able to communicate your ideas, analyses and conclusions effectively to a variety of stakeholders.

This subject will equip you with the written, oral and interpersonal communication skills required to survive and thrive in a scientific workplace. Through seminars and interactive workshops, you will be exposed to a wide range of communication elements, from how to craft the perfect email to working in culturally diverse settings.

You will be given regular opportunities to practise and develop your skills, give and receive feedback and work in a variety of group settings to improve your teamwork and interpersonal skills. Understanding your own communication preferences is another key aspect of this subject.

All assessment tasks in this subject are modelled around real-world activities you will encounter in the workplace and will enable you to develop your professional skills.

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