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    Faculty of Technology (Computing Sciences and Engineering) Logo
  • Study Type

    Full time & Part time available

  • Subject Areas

    Computer Science

    Engineering

  • Start Date

    September, January

  • Course Duration

    One year full-time (with optional one year placement available), 18 months for January starts, two to six years part-time or by distance learning

  • Course Type

    MSc, PgDip, PgCert

  • Course Fees

    website

  • Last Updated

    19 March 2019

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 opportunities to apply what you learn in areas such as robot control and games development, depending on your interests. 

Modules include work based on research by the Centre of Computational Intelligence (CCI). 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. Past students have published papers with their CCI project supervisors and gone on to PhD study.

Course modules

  • Computational Intelligence Research Methods details quantitative and qualitative approaches including laboratory evaluation, surveys, case studies and action research.
  • Artificial Intelligence (AI) Programming presents a logical programming approach. AI programming is a key skill and a necessary tool for problem solving in industry.
  • Mobile Robots discusses the hardware and software architectures used to build mobile robot systems.
  • Fuzzy Logic considers the various fuzzy paradigms that have become established as computational tools.
  • Artificial Neural Networks appraises neural network computing from an engineering approach and the use of networks for cognitive modelling.
  •  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.
  • 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.
  • 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.

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 full-time students can expect to spend around 40 hours on independent study each week.

Graduate Careers

Graduates typically follow careers within robotics programming and research, games development, control systems, software engineering, internet businesses, financial services, mobile communications, programming, software engineering and many more. Opportunities also exist for further academic study toward a PhD and a career in research. 


Visit the Intelligent Systems and Robotics MSc/PG Dip/PG Cert page on the De Montfort University website for more details!

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