Applied Artificial Intelligence MSc (Distance Learning)

Organisational leadership requires the support of strategic information to make decisions that align with institutional objectives. In this module you will critically explore and evaluate the leadership opportunities presented by access to strategic information within an organisation. You will study a range of approaches to managing strategic information at scale, and you will develop knowledge that demonstrates your critical understanding of the impact of both traditional and emerging approaches and issues to managing strategic information.

This module aims to provide you with skills that are key to helping you become a successful computing researcher or practitioner. You will get the opportunity to study topics including the nature of research, the scientific method, research methods, literature review and referencing. The module aims to cover the structure of research papers and project reports, reviewing research papers, ethical issues (including plagiarism), defining projects, project management, writing project proposals and making presentations.

Autonomous systems are intelligent systems that can act independently to accomplish goals based on their knowledge and understanding of their environment and the tasks they have to complete. This module aims to cover the background and requirements for intelligent systems autonomy in a wide range of applications, taken from a computer science and software-oriented viewpoint. As well as the technical challenges of system autonomy, you’ll get the opportunity to study ethical and legal issues, and human factors implications.

Data mining is a collection of tools, methods and statistical techniques for exploring and extracting meaningful information from large data sets. It is a rapidly growing field due to the increasing quantity of data gathered by organisations. There is a potential high value in discovering the patterns contained within such data collections. In this module you will look at different data mining techniques and use appropriate data-mining tools in order to evaluate the quality of the discovered knowledge. You will study approaches to preparing data for exploration, supervised and un-supervised approaches to data mining, exploring unstructured data and the social impact of data mining. You will be expected to develop your knowledge such that you are able to contribute to discussions around current application areas and research topics and to increase your background knowledge and understanding of issues and developments associated with data mining.

The purpose of this module is to enable you to appreciate the historical, current and future application areas of Artificial Intelligence and Data Analytics in relation to both theoretical and practical aspects and to investigate at least one application area in depth. Case studies discussed in the sessions will provide an exploration of applications in a variety of different areas and will be achieved by combinations of study of current research papers, tutors’ own research and the investigative work of the students within the module.

The Robotics module allows you to gain specialist knowledge in robotic devices and autonomous applications by examining the integration of mechanical devices, sensors and ‘intelligent’ computerised robotic agents. You will also explore the latest developments in robotics and intelligent systems through a series of investigative tasks and practical sessions. The module covers essential techniques for the design and development of robotic based systems using a collection of robotic hardware and simulation software. It supports the discussion and analysis of the hardware and software used to build real-world robotic systems. It introduces device and architectural specific topics required to enable students to design and develop software for intelligent autonomous robots. This will include low-level programming of I/O devices for robotic swarms, sensor systems and active modelling and simulation. It will introduce planning for intelligent robots taking a lifecycle approach from theory to activation.

Machine Learning techniques are now used widely in a range of applications either stand-alone or integrated with other AI techniques. The Machine Learning module allows you to obtain a fundamental understanding of the subject as a whole: how to embody machines with the ability to learn how to recognise, classify, decide, plan, revise, optimise etc. You will learn which machine learning techniques are appropriate for which learning problem, and what the advantages and disadvantages are for a range of ML techniques. We will consider the widely known data-driven approaches, and specific techniques such as “deep learning”, and investigate the typical applications and potential limitations of these approaches. We will introduce available tools and use them in practical classes, evaluating learning bias and characteristics of training sets. High profile applications of data driven, stand-alone, ML systems will be investigated, such as the AlphaGo method. Where data is sparse, and knowledge is already present in a system, we will investigate methods to improve heuristics of existing AI systems, and to learn or revise domain knowledge. This is essentially the area of model-driven ML, where is often integrated to other reasoning systems.

This module enables you to work independently on a project related to a self-selected problem. A key feature in this final stage of the course is that you will be encouraged to undertake an in-company project with an external Client. Where appropriate, however, the Project may be undertaken with an internal Client - research-active staff - on larger research and knowledge transfer projects. The Project is intended to be integrative, a culmination of knowledge, skills, competencies and experiences acquired in other modules, coupled with further development of these assets. In the case where an external client is involved, both the Client and Student will be required to sign a learning agreement that clearly outlines scope, responsibilities and ownership of the project and its products or other deliverables. The Project will be student-driven, with the clear onus on you to negotiate agreement, and communicate effectively, with all parties involved at each stage of the Project.