Artificial intelligence (AI) encompasses the techniques and methods used to tackle problems that traditional approaches to computing struggle to solve. MSc in Artificial Intelligence will allow for the acquisition of knowledge about artificial intelligence and, most importantly, the translation of intelligence into machines so the machines can make smarter decisions. This degree program provides a wider exposure to AI from the perspectives of computational intelligence, natural language processing, and robotics.
The modules feature fuzzy logic, artificial neural networks, evolutionary computing, mobile robotics, deep learning, and applied computational intelligence for providing theoretically sound solutions to real-world decision-making and prediction problems.
- This course is accredited by BCS, The Chartered Institute for IT, for the purposes of fully meeting the academic requirement for registration as a Chartered IT Professional.
- Our internationally recognised Institute of Artificial Intelligence (IAI) helps to inform the content of our course, allowing you to understand the current research issues related to artificial intelligence and potential areas that need more research focus.
- The modules you’ll study feature work based on research by our IAI and focus on the use of fuzzy logic, artificial neural networks, evolutionary computing, mobile robotics and biomedical informatics, providing theoretically sound solutions to real-world decision-making and prediction problems.
- Your studies can work around your work and other commitments with full-time, part-time or distance learning study options available. This makes the course ideal for both recent graduates and professionals already in employment.
- Artificial intelligence is a growing industry across the globe. Employment opportunities exist in areas such as games development, control systems, software engineering, internet businesses, financial services, mobile communications, programming, and software engineering.
- Computational Intelligence Research Methods details quantitative and qualitative approaches including laboratory evaluation, surveys, case studies and action research.
- Fuzzy Logic considers the various fuzzy paradigms that have become established as computational tools.
- Natural Language Processing focuses on Natural Language Processing (NLP) using Python. It uses NLTK and Pytorch. NLTK is a leading platform for NLP which provides a number of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries. Pytorch provides access to deep learning function which can be applied to NLP problems.
- Mobile Robots discusses the hardware and software architectures used to build mobile robot systems.
- Computational Intelligence Optimisation (CIO) is a subject that integrates artificial intelligence into algorithms for solving optimisation problems that could not be solved by exact methods. Thus, CIO is the subject that defines and designs metaheuristics, i.e. general purpose algorithms. This makes CIO the subject that tackles optimisation problems in engineering, economics, and applied science
- Artificial Neural Networks and Deep Learning appraises neural network computing from an engineering approach and the use of networks for cognitive modelling.
- Applied Computational Intelligence considers knowledge-based systems; the historical, philosophical and future implications of AI; then focuses on current research and applications in the area
- Intelligent Mobile Robots covers sensing, representing, modelling of the environment, adaptive behaviour and social behaviour of robots. OR
- Individual Project provides the opportunity to demonstrate skills acquired from the course in a problem solving capacity. This typically involves the analysis, design and implementation of a computer system.
Note: All modules are indicative and based on the current academic session. Course information is correct at the time of publication and is subject to review. Exact modules may, therefore, vary for your intake in order to keep content current. If there are changes to your course we will, where reasonable, take steps to inform you as appropriate.
Teaching and assessments
The course consists of an induction unit, eight modules and an individual project. The summer period is devoted to work on the project for full-time students. If you choose to study via distance learning, you would normally take either one module per semester for four years or two modules per semester for four years plus a further year for the project.
Teaching is normally delivered through lectures, seminars, tutorials, workshops, discussions and e-learning packages. Assessment is via coursework only and will usually involve a combination of individual and group work, presentations, essays, reports and projects.
Distance learning material is delivered primarily through our virtual learning environment. Books, DVDs and other learning materials will be sent to you. We aim to replicate the on-site experience as fully as possible by using electronic discussion groups, encouraging contact with tutors through a variety of mediums.
Contact and learning hours
On-site students will have the lessons delivered by the module tutors in slots of three hours. In the full-time route, you can expect to have around 12 hours of timetabled taught sessions each week, with approximately 28 additional hours of independent study. There are also three non-teaching weeks when fulltime students can expect to spend around 40 hours on independent study each week.
Taught by experienced research staff from the Centre for Computational Intelligence (CCI), an internationally recognised centre highly rated in the most recent Government Research Assessment Exercise, you will gain a professional qualification that gives substantially enhanced career and research prospects in both traditional computing areas and in the expanding area of computational intelligence.