Artificial Intelligence and Autonomous Systems

Unit Description 


AI System Design 

The unit covers system design for complex artificial intelligence (AI) systems to maximise the capabilities of such systems. Upon completion, students should be able to design complex AI systems from a top-down perspective for real-world application domains. Topics include decision analysis, design and development cycles, functional analysis, knowledge design, reliability and usability analysis, system design, system integration, system modelling and simulation, system optimisation, system test and evaluation, and systems theory.  


Artificial Intelligence and Intelligent Agents 
This unit gives an overview of the application and associated theories of artificial intelligence (AI) and intelligent agents. Upon completion, students should be able to evaluate AI and intelligent agents’ techniques and manage the development of AI systems to solve problems in the real world. Topics included are communicating and acting, concepts of problem-solving, ethical considerations for AI systems, introduction to AI and intelligent agents, knowledge and reasoning, natural language processing, and uncertain knowledge and reasoning.


Data Visualisation and Simulation 
This unit aims to give students: the background for data visualisation, data storytelling, data visualisation techniques in non-immersive and immersive environments, data visualisation for artificial intelligence, innovative data exploration and discovery of hidden patterns, interactive techniques and user interactions with data, interactive analytics, non-real-time and real-time simulation of live-steaming and offline data, and the design and development of interactive and collaborative/multi-user data visualisation tools using Python, JavaScript D3.js, Unity3D, Unreal, WebGL, and XR technologies and devices. 


This unit focuses on database design, implementation and management. Topics include data modelling, database administration, logical and physical database design, non-relational databases, recovery, relational model, security, standard query language (SQL) and transaction management. The theory material is complemented by practical work using common database management systems. 


Information Technology Project Management 
This unit identifies, explains and explores a project management approach to information and communications technology (ICT) systems development. In particular, the content is specifically designed to deliver practical insights into managing and coordinating the activities of an ICT project. 



Each topic in the unit covers content that explains the journey from project initiation to project closure, and students can gain an understanding of these issues by assessing and making decisions about a real-world scenario. As a part of this knowledge and skill development, students will learn about key aspects of cost management, procurement management, project analysis, project integration management (PIM), quality management, resource management, risk management, scheduling, scope management, and stakeholder and communications management. Additionally, students can gain an understanding of key documentation and industry standards, which can help them in their careers. 


Introduction to ICT Research Methods 
This unit provides an introduction to research in the information and communications technology (ICT) discipline. It explores the kinds of research questions addressed in ICT research and provides an opportunity for students to understand the broad range of research approaches used in ICT research including action research, case study research, design research, experimental research and survey research.  



Students will develop both research and project management skills, and gain the knowledge and skills needed to critically evaluate the ICT research literature. 


IT Professional Practice Project 

Students in this team-based unit will use information technology (IT) approaches to solve real-world problems from a range of domains. The creation of relevant project deliverables will require students to build upon skills developed during their studies. They will learn to appreciate the interdisciplinary nature of their project and how the skills of team members from different IT majors are required to solve complex problems. Project management and communication with clients and other stakeholders in a professional manner will be emphasised.  


Machine Learning

The objective of this unit is to introduce important concepts in machine learning and important algorithms in this field.  


Topics include classification and clustering algorithms, data pre-processing, ensemble learning, hyperparameter tuning, linear and logistic regression, model evaluation, neural networks and deep learning. 



Application examples are taken from areas such as medical decision-making, sentiment analysis and computer vision. Students will learn how to design and implement machine learning and deep learning models for data analysis using Python. 


Principles of Computer Science 

This unit is designed to develop problem-solving and programme design skills by using an object-oriented programming language. Major topics include algorithm design, procedural abstractions, use of libraries as collection of black-box code modules, the concepts of pre- and post-conditions, strings, arrays, an introduction to object-oriented concepts including data abstraction, encapsulation, classes and object references, inheritance, introduction to recursion, streams and file input and output, and the definition and use of common classes — lists, stack and queues.  


Systems Analysis and Design 
This unit introduces methods and techniques for analysing problematic organisational situations, particularly those leading to the development of an information system, and it draws on both technical and organisational materials to provide the knowledge and skills necessary to design and implement an operational system.