The course includes basic concepts in computer data organization and information processing; hardware, physical organization, and access methods for fine storage; file I/O; introduction to database systems.

Formerly CT2020 This course is an introduction to the theory, design and programming of modern user interfaces. Topics will include: human factors; interaction design; usability; software development with graphical user interfaces (GUI) for computers, game consoles and mobile devices; input and output devices (including game controllers).

Formerly CT3060 Introduction, history, and applications; scanning and quantization; visual perception; output devices; pattern recognition; feature extraction; decision theory; classification rules; data representation and formats; image enhancement and restoration; edge detection; segmentation and texture; correlation and registration.

This course will act as a comprehensive introduction to the concepts and issues in new media technologies with an emphasis on computer games. Students will gain an appreciation of the broad range of perspectives which new media and games encompass, including graphics realism and aesthetics, culture, sociology, psychology, philosophy and literary studies. Students will be expected to work in small groups to develop several small and one intermediate length games using commercial game engines.

Formerly CT3110 Graphical input and output devices; segments; interactive input techniques; user interface design; windowing and clipping; 2D and 3D transformation; 3D modelling and viewing; hidden-line and hidden-surface removal.

Formerly CT3120 A project-based course dealing with the design and implementation of behaviour-based robots to accomplish specific tasks. Students work in groups and are introduced to concepts in sensor technologies, sensor data processing, motion control, embedded system design, real-time programming and behaviour arbitration.

Formerly CT3130 Introduction to computer communication networks. Digital data and voice transmission. Protocols for error and flow control, media access for LANs and MANs, routing and condition control, interconnection of networks. Introduction to recent advances in networks.

This course gives an introduction to artificial intelligence (AI). The course covers supervised classification based on e.g., artificial neural networks - deep learning, as well as unsupervised learning - clustering, regression, optimizing and reinforcement learning, as well as design of experiments and evaluation. Students will learn how this knowledge is used in areas such as: building smart robots, computer vision and other applications.

Digital circuits, combinational systems, memory, register transfer, control logic design, CPU design, and advanced topics on micro-architectures.

This course focuses on the principles and knowledge of software engineering. It covers the approaches taken in developing large programming projects, including requirements analysis, specification, design (e.g., top-down modularization), coding (e.g., structured programming), debugging and testing, maintenance, and thorough documentation.

Formerly CT3790 You will be introduced to concepts and features commonly found in operating systems. Class discussion will concentrate on traditional operating system topics (processes, memory management, file systems, input/output) as well as distributed operating system topics (communication, synchronization, and distributed file systems). UNIX will be studied as an example of traditional and distributed operating systems.

A comparison of programming languages and their implementations. This course will survey the major types of programming languages. Topics include programming language paradigms (imperative, logic, functional and object-oriented), parameter passing, control structures, data types, typing mechanisms, binding rules, heap management, procedural languages, historical developments.

This course introduces students to various aspects of Internet and web technologies such as HTML/XHTML, CSS, XML, and JavaScript/Java through research and project related assignments. The exact topics covered in this course will be at the discretion of the instructor.

Formerly CT3995 The content of this course will include topics and issues which are currently of interest to the general computing community.

An introduction into advanced topics in the theory of computation. Topics include: basics of formal languages, models of computers, regular expressions, deterministic and nondeterministic finite automata, computability and computational complexity.

Introduction to image processing and visual perception. The course will explore topics such as: data representation and formats; image enhancement and restoration; edge detection; segmentation and texture; correlation and registration. These fundamentals will then be applied to the analysis of images by using pattern recognition; feature extraction and detection; object tracking, decision theory; classification rules.

A project-based course dealing with the design and implementation of behaviour-based robots to accomplish specific tasks. Students work in groups and are introduced to concepts in sensor technologies, sensor data processing, motion control, embedded system design, real-time programming and behaviour arbitration.

Security stands out as a critical requirement in the design and deployment of information systems in general, and computer networks in particular. This course provides a comprehensive coverage of the theory, concept, design principles and technologies for information security. This course deals with the design of secure information systems with emphasis on secure networking and secure transfer of information.

This course focuses on the fundamentals of Data Science methods to extract nontrivial, previously unknown, and potentially useful information from databases. It covers data exploration and preparation, data visualization, and computing with data using machine learning algorithms for tasks such as classification, clustering, and outlier detection in structured datasets, along with model evaluation techniques.

This course is intended to be taken during the summer between Year 3 and Year 4. The student will be expected to be working full time for a minimum of 12 weeks. The course will require a summative evaluation by the employer, as well the student needs to maintain a logbook of work activities undertaken and a post-employment presentation to supervising faculty