Computing MSc

How do you combine information from Web sources that are using different terminology? This is the fundamental question underlying the idea of the Semantic Web. The answer is the use of graph-based structures that describe a kind of common vocabulary for a particular domain, and annotation that describes information sources in terms of this vocabulary. Then, information can be queried similar to having a (distributed) database. This module will cover basic languages for describing ontologies, as well as languages and tools for processing and querying them. Working both individually and in teams, you will get hands-on experience in using semantic web technologies. In addition, you will be introduced to industry practice around concepts of so-called Linked Data and Knowledge Graphs.

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.

This module considers how the Internet can be used to provide services, such as the web enabled provision of information, cloud computing and VoIP (Voice over Internet Protocol). As well as providing a service the Internet can also be used as a medium for the control of remote agents, such as robotic devices, and within this you’ll consider the technologies that facilitate the provision of remote access control. This module also provides you with the opportunity to explore contemporary research areas regarding Internet related subjects.

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.

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 unsupervised 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.

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.

This module brings together database, object-oriented semantics and web authoring skills using an appropriate set of development tools to enable the student to construct distinct software artefacts. You will be introduced to the programming and design techniques used to produce information systems that meet their required specifications. This will involve the modelling of business activity, the information that supports decision making and instances of significant events and actions. You will acquire skills in programming languages capable of implementing object-oriented and web script software and will also be able to select and apply design techniques to enable an appropriate choice of semantic components and implemented software components to meet the requirements of a given software system.

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.

You 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, which is often integrated into other reasoning systems.

The data needs of modern enterprises and organisations require a more flexible approach to data management than that offered by traditional relational database management systems. With organisations increasingly looking to Big Data to provide valuable business insights, it has become clear that new approaches are required to handle these new data requirements. Primarily focusing on non-relational data models, this module introduces you to alternative approaches to modelling the data needs of an organisation. It also provides you with an opportunity to use non-relational databases and database technologies to build robust and effective organisational information systems.