Master Of Software Engineering (Artificial Intelligence Advanced)

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In this subject the students are introduced to the main project management principles and modern software project management practices. During the subject, the different methods for managing and optimising the software development process are discussed along with the different techniques for performing each phase of the software development life cycle.

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In this subject students are introduced to the current Software Engineering standards and processes, with the aim of enabling them to analyse, design, and implement software projects that follow certain quality measures at every stage of the Software Development Life Cycle. The subject covers requirements engineering, modelling and design of software, software architecture, verification and validation of software systems, and other topics that are related to software engineering practices.

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The intended aim of this subject is to equip students with fundamentals of Secure by Design and enable abstraction of its underlying key principles. The course content is oriented towards the core pillars of Information Security: Confidentiality, Integrity and Availability. The subject is structured around the main Secure Development Lifecycle (SDLC) Models, Security by Design principles, appropriate SDLC model selection, application of secure development techniques, vulnerabilities and techniques to tackle, secure design and development best practices, introduction to encryption, introduction to the classification of security flaws and application security.

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This subject helps students explore several important fields of general inquiry pertaining to significant intellectual issues related to human beings so they can view everyday problems and formulate solutions in new ways. Broadly, the subject covers the theory of knowledge, human cognition, ethical and moral values, analysis of human history, critical analysis, appreciation of literature and arts and social interaction among human beings through a technological context. Human Centered Design is to give students an appreciation of the factors that influence human behavior and interactions so that they can apply specialised skills to help solve problems that affect diverse societies.

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This subject aims to give a broad introduction of intelligent systems, that is, how technologically advanced machines perceive and respond to the world around them. Discussion will focus on how Artificial Intelligence (AI) concepts and classifications are used to design intelligent systems. Overview of AI topics such as representation, reasoning, search methods, intelligent agents, learning, uncertainties and probabilities, perception and action, and communication are presented. It also includes discussions of AI classifications such as Machine Learning, Robotic, Natural Language Processing, Speech Recognition, Expert Systems, Computer Vision, and how they are used to make intelligent systems. This subject also enables students to understand the particular ethical issues that AI presents and how it can be used to benefit society.

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This subject introduces students to a framework for developing good scholarly inquiry skills and fundamental knowledge needed to make rational decisions about research strategies. Students will be presented with research strategies to critically investigate exemplar studies and examine the connection between a research question with appropriate research design and methodology. On completion of this subject, students should be able to develop researchable questions, and write research proposals and literature reviews. They will have a critical understanding of the strengths and limitations of the quantitative, qualitative and mixed method approaches to research. They will also learn about the ethical principles of research, challenges in getting approval and the approval processes.

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The purpose of this subject is to provide a solid mathematical background that students will encounter in studies of Artificial Intelligence, specifically in sub-areas such as machine learning, natural language processing, speech recognition, and computer vision. This subject will cover topics including linear algebra (equations, functions and graphs), differentiation and optimisation, vectors and matrices, statistics and probabilities. Thorough understanding of these mathematical concepts is necessary for understanding the inner workings of the algorithms in Artificial Intelligence.

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This subject is designed to give a graduate-level student an in-depth understanding of the methodologies, technologies, mathematics and algorithms currently used in machine learning. Students will learn the theory behind a range of machine learning tools and practice applying the tools to different applications. It covers topics such as classification, linear models, learning theory, generative models, graphical models and learning paradigm. This subject covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam’s Razor. Students are given short programming assignments that include hands-on experiments using machine learning algorithms and methods.

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This subject builds on the skills and knowledge students acquired from Machine Learning Principles and focuses on deep learning. It introduces students to foundational topics on neural networks, its applications to sequence modelling, computer vision, generative models and reinforcement learning. Focus will be given on learning how to model and train neural networks to implement a variety of computer vision applications. Students will be presented with practical examples of how to develop applications using deep learning. Knowledge in programming and understanding of machine learning concepts is required in this subject.