Artificial Intelligence MSc

This module aims to provide you with skills that are key to helping you become a successful computing researcher or practitioner. You'll 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 reports 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. This module looks at different data mining techniques and gives students the chance to use appropriate data-mining tools in order to evaluate the quality of the discovered knowledge. Topics studied include looking at the value of data; approaches to preparing data for exploration; supervised and un-supervised approaches to data mining; exploring unstructured data; social impact of data mining. Current application areas and research topics in data mining will also be discussed and students will be expected to develop their knowledge such that they are able to contribute to such discussions and to increase their background knowledge and understanding of issues and developments associated with data mining.

Knowledge representation and reasoning (KR) is the field of artificial intelligence dedicated to representing information about the world in a form that computer systems can manipulate and utilize to solve complex tasks such as making decisions, diagnosing a medical condition, finding suitable answers to queries or having a dialog in a natural language. Specific KR languages have been developed to express representations. Once information representations are established, reasoning algorithms can be applied to draw conclusions from the available information in a traceable, explainable way. Each KR language is supported by such reasoning algorithms. KR is at the heart of the area of the semantic web, and has found deployment in big corporations such as Google and Amazon in the form of knowledge graphs. This module will enable learners to familiarize themselves with principles and algorithms of knowledge representation and reasoning, and gain experience in using them to solve practical problems.

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 the learning system is often integrated to other reasoning systems.

The purpose of this module is to enable students to appreciate the historical, current and future application areas of AI and DA 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 & 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 an 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.

This module will recap on the history of automated planning from the days of STRIPS, up to the present day. It will focus on the kinds of assumptions, algorithms, heuristics and representation languages that have been used to create generative planning algorithms. It will illustrate these developments using a range of planning engines and planning platforms. Current application areas and research topics in automated planning, such as hybrid planning, will be discussed and students will be expected to develop their knowledge such that they are able to contribute to such discussions and to increase their background knowledge and understanding of issues and developments associated with AI Planning.

This module enables the student to work independently on a project related to a self-selected problem. A key feature in this final stage of the MSc is that students 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 the student to negotiate agreement, and communicate effectively, with all parties involved at each stage of the Project.

You will be taught through a series of lectures, tutorials, practical's in computer labs and independent study. Assessments will be based on your choice of modules; this can include presentations, essays, technical documents and peer review. The project is based on your choice of specialism.

Your module specification/course handbook will provide full details of the assessment criteria applying to your course.Assessment will include coursework and peer review and reflect the emphasis of the course on the ability to apply knowledge and skills.

Feedback (either written and/or verbal) is normally provided on all coursework submissions within three term time weeks – unless the submission was made towards the end of the session in which case feedback would be available on request after the formal publication of results. Feedback on final coursework is available on request after the publication of results

Further information

The teaching year normally starts in September with breaks at Christmas and Easter, finishing with a main examination/assessment period around May/June. Timetables are normally available one month before registration.