Human intelligence involves a continuous process of sensing, analysing, reasoning, and adaptation. In this course, you will focus on studying algorithms (both learning and optimisation), which could be used to develop an artificial intelligence (AI) system. You will focus in particular on theory and implementation using different programming languages. This module will provide you with opportunities to comprehend, define, and formulate real-world problems, integrate learning and optimisation algorithms such as artificial neural networks and genetic algorithms, and select optimal machine learning, training, and reasoning algorithms.

This module’s main focus is on the fundamentals of machine learning (ML), covering both basic and advanced concepts, including philosophy, linear algebra, big data, optimisation, and information theory. You will study some of the key AI models such as deterministic models, stochastic models, Markov chain, linear regression, logistic regression, gradient descent, softmax regression, principal component analysis (PCA) and eigenvectors, multilayer perceptron (MLP) and backpropagation, amongst others. There is a strong emphasis on tying the underlying theory to applications to real-world situations. After studying this module, you will have a comprehensive understanding of ML algorithms and have in-depth computational knowledge, problem formulation, and be able to code and apply these ML algorithms.

This module is designed as an introduction to some of the central concepts and tools used in data science. As such it includes discussions on data capture, maintenance, processing, analysis and communication. You will examine a series of real-world case studies, and state-of-the-art software and algorithms will be used as tools within these case studies. You will explore: an introduction to data science; data capture; acquisition of data; data maintenance; data processing; data analysis; data communication; and intelligent decision-making. On completion of this module you will be able to extract meaning from data and communicate that meaning to non-specialist audiences, and you will be able to use software in the application of specialist algorithms and approaches to data processing and analysis.

This module looks at different data processing and mining techniques and state-of-the-art data processing and mining tools. The topics you will explore include an introduction to data mining and knowledge discovery process, data collection and processing with data types from a variety of sources, and social web mining. You will look at a variety of techniques and trends in data mining and develop an understanding of: data models; the knowledge discovery process; data selection; pre-processing and cleaning; data mining algorithms and association rules; classification; clustering; text mining; social media mining; and data visualisation. After studying this module, you will be able to use all aspects of the knowledge discovery process from data collection and data processing to representation, applying cutting-edge data mining techniques to extract information from data. You will also develop your own data mining solution using a state-of-the-art data mining tool.

This module provides you with hands-on, cutting-edge AI training with application to a wide range of real-world problems, including audio-visual speech processing, image and object recognition, fraud detection, deception detection and anomaly detection. This module will provide you with opportunities to: study concepts such as multimodal and context-aware computing; understand both basic and advanced pre-processing required for different data types (e.g.audio, video, text, EEG); and learn the application of optimisation, big data, and deep learning algorithms using popular AI and machine learning tools. There is a strong emphasis on coupling the underlying theory to an application to real-world situations. After studying this module, you will be able to demonstrate a thorough understanding of efficient context-aware strategies, required to comply with different application requirements and constraints, comprising trade-offs among computational complexity, processing time, and available datasets, and will be capable of solving a wide range of real-world problems using AI tools.

Intelligent agents are programs that can perceive their environment through sensors and can change the state of their environment through actuators. Environments can be fully software-based such as a computer game in which an agent can learn how to play or a physical real-world environment where the agent controls a robot and helps it to navigate around obstacles to reach its destination. You will explore topics including: logic based and reactive architectures; layered architectures; temporal difference learning; TD-Gammon – SARSA; and Q learning – A3C algorithm. After studying this module, you will have a comprehensive understanding of the principles of reinforcement learning and how it can be used in practical scenarios and be able to evaluate alternative architectures for the design and implementation of intelligent agents.

Data visualisation is an important visual method for effective communication and analysing large datasets. With the rapid increase of data volume and complexity, users often find it difficult to understand and make sense of the data. Through data visualisation, data is presented in a meaningful and interactive way to users, so that they can draw conclusions from data that sometimes are not immediately obvious, and hence manipulate and use the data for exploration, learning, communication, problem resolution and decision-making. The module will provide you with the knowledge of theoretical concepts of interactive data visualisation and presentation, creative design processes as well as the applications of interactive visualisation and presentation tools and techniques. You will explore: history of data visualisation; planning a visualisation; designing a narrative around data; how to create the right charts for your data; elements of poor design; perception; interactive data visualisations; use of software; and communication and telling a story with data. On completion of this module you will be able to understand and apply the fundamental concepts and techniques in data visualisation and use specialist software such as Tableau to produce visual insights into data and to present your findings to a non-specialist audience.

In this module you will learn to use a wide variety of tools, techniques, methodologies and processes in the field of project management. Ethical and legal aspects of project planning and management will also be discussed. You will explore: project management and associated risks; risk handling techniques and methodologies; management of organisational resources and budgeting; legal, social, ethical and regulatory requirements and issues; planning, estimation, structure of projects; monitoring and control; review of scientific processes; and employability issues. You will develop a comprehensive understanding of the concepts of project management and its crucial operation in complex and unpredictable environments. By studying this module, you will develop a critical understanding of methodologies underpinning core project management processes and procedures to assure project success and minimise risk, and be able to flexibly and autonomously apply knowledge of solutions and processes that underpin professional practice in project management.

This module explores the concepts and practice of virtualisation and cloud computing. You will examine the associated solutions commonly used in industry and you will develop an understanding of the key aspects and principles of their operation. After studying this module, you will be able to demonstrate a critical understanding of the concepts, issues and considerations of virtualisation and cloud computing, and demonstrate mastery of the tools and advanced techniques used in the development of sophisticated solutions.

This module introduces you to research and methodologies used to underpin scientific work, data analysis, hypothesis establishment and artefact validation in understanding research of an appropriate subject discipline. The module explores: an introduction to research; literature search, discussion and critical evaluation; research methodologies and strategies; experimental procedures; data analysis methods; research philosophy; identification of a suitable project title; research impact and gap analysis; production of scientific work; consistency, rigour and clarity; ethics in research; and legal, social and regulatory requirements and issues. You will be actively engaged in enquiry during the learning process and have to articulate your own understanding of the issues examined during teaching sessions. You will understand the concepts of research and its crucial operation in complex and unpredictable environments where a range of techniques and technologies coexist to identify gaps in the existing literature. You will develop your collaborative abilities through the learning process as part of the task to cross-fertilise and discuss research ideas about the technologies analysed and their core implementation challenges and benefits with fellow students. You will be given the opportunity to select and examine specific research problems and to reflect and exercise research methods, title decomposition and research question formulations. By studying this module, you will develop a critical evaluation and understanding of knowledge on various research principles and methodologies and be able to flexibly and autonomously apply knowledge of solutions to open research problems, demonstrating the ability to understand research impact and develop gap analysis tools and techniques.

One of the most important skills expected of being an effective specialist in any discipline is the ability to work creatively under the appropriate guidance of an organisation. It is therefore essential that you use and develop the skills and knowledge gained from other modules and from your wider educational or working background in a major integrative exercise. In this module, you will be expected to develop an idea and demonstrate your ability to develop it further by applying your technical, analytical, practical and managerial skills in an integrated manner. This module will support you to autonomously develop your research skills and problem-solving skills to build a novel artefact that encompasses the theoretical domains and hands-on analysis and ideation during your course. You will engage in an informative tutorial on how to apply your knowledge from research methods in the design of your artefact and research questions. This module will enable you to demonstrate a deep and systematic understanding of a selected specialist area of your course, including issues at the forefront of research or professional practice in your chosen area. You will be able to independently design and undertake a substantial investigation that rigorously tackles a complex problem in computer science, selecting appropriate methodological processes and critically evaluating their effectiveness.