Computational Intelligence (CI) encompasses the techniques and methods used to tackle problems that traditional approaches to computing struggle to solve. The four areas of fuzzy logic, neural networks, CI optimisation and knowledge-based systems encompass much of what is considered to be computational (or artificial) intelligence. There are also opportunities to apply what you learn in areas such as robot control and games development, depending on your interests.
You can choose from a number of specialist modules including natural language processing, artificial neural networks and data mining techniques, while developing your skills in our dedicated robotics laboratory, equipped with a variety of mobile robots. The applied computational intelligence module considers knowledge-based systems, as well as the historical, philosophical and future implications of AI, and focuses on current research and applications in the area.
Modules include work based on research by the Institute of Artificial Intelligence (IAI). With an established international reputation, its work focuses 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. Previous students have published papers with their IAI project supervisors and progressed on to PhD study.
- 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.
- Take advantage of our AI laboratory featuring cutting-edge workstations and technologies such as the Emotiv Flex Gel Sensor Kit and Emotiv PRO, Lynxmotion Hexapod robot, Turtlebots, HTC Vive development kits, 3D Scanner, 3D Printer and Lego EV3 Kits.
- 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
- Data Mining, Techniques and Applications examines the tools and techniques needed to mine the large quantities of data generated in today’s information age. It provides practical experience as well as consideration of research and application areas
- 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.
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.
We have our own Advanced Mobile Robotics & Intelligent Agents Laboratory situated in Gateway House.
The Advanced Mobile Robotics & Intelligent Agents Laboratory contains a variety of mobile robots, providing excellent resources for teaching and research.
Mobile Robotics is taught as an option at undergraduate level as well as on the Artificial Intelligence with Robotics BSc programme. On the Intelligent Systems and Robotics MSc programme students will be exposed to the more advanced techniques.
The Centre for Computational Intelligence (CCI) conducts research into use of computational intelligence techniques on mobile robots and encourages PhD applications in this field.
Library and learning zones
On campus, the main Kimberlin Library offers a space where you can work, study and access a vast range of print materials, with computer stations, laptops, plasma screens and assistive technology also available.
As well as providing a physical space in which to work, we offer online tools to support your studies, and our extensive online collection of resources accessible from our Library website, e-books, specialised databases and electronic journals and films which can be remotely accessed from anywhere you choose.
We will support you to confidently use a huge range of learning technologies, including Blackboard, Collaborate Ultra, DMU Replay, MS Teams, Turnitin and more. Alongside this, you can access LinkedIn Learning and learn how to use Microsoft 365, and study support software such as mind mapping and note-taking through our new Digital Student Skills Hub.
The library staff offer additional support to students, including help with academic writing, research strategies, literature searching, reference management and assistive technology. There is also a ‘Just Ask’ service for help and advice, live LibChat, online workshops, tutorials and drop-ins available from our Learning Services, and weekly library live chat sessions that give you the chance to ask the library teams for help.
More flexible ways to learn
We offer an equitable and inclusive approach to learning and teaching for all our students. Known as the Universal Design for Learning (UDL), our teaching approach has been recognised as sector leading. UDL means we offer a wide variety of support, facilities and technology to all students, including those with disabilities and specific learning differences.
Just one of the ways we do this is by using ‘DMU Replay’ – a technology providing all students with anytime access to audio and/or visual material of lectures. This means students can revise taught material in a way that suits them best, whether it’s replaying a recording of a class or adapting written material shared in class using specialist software.