MSc Intelligent Systems/MSc Intelligent Systems and Robotics

Start date: September and January.

Duration: September start: One year full-time January start: 18 months full-time (with optional one year placement available), two to six years part-time or distance learning.

Entry requirements: You should have the equivalent of a British Honours degree (2:2 minimum) in a relevant subject. Most science and engineering-based subjects are acceptable, as long as you have some experience of computer programming. If you have no formal academic qualifications, but do have extensive industry experience, we will consider your application on an individual basis.

English language requirements: IELTS 6.0 with no component below 5.5 or equivalent.

Tuition fees: Please visit UK/EU tuition fees and scholarships or International tuition fees and scholarships and Postgraduate funding 2017/18 for information.

How to apply: Please visit dmu.ac.uk/pgapply for information.

Contact details:
T: +44 (0)116 2 50 60 70
E: enquiry@dmu.ac.uk
W: dmu.ac.uk/technology

Programme Leader – Prof. Francisco Chiclana E: chiclana@dmu.ac.uk
Capitalising on the growth in interest in artificial intelligence and intelligent robotics, Intelligent Systems MSc and Intelligent Systems and Robotics MSc at DMU will provide you with knowledge of the various models of computational intelligence, skills in the associated computational techniques, an insight into their theoretical basis and the ability to apply these techniques to a wide variety of problems.

Computational Intelligence (CI) encompasses the techniques and methods used to tackle problems poorly solved by traditional approaches to computing. 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. You will have an opportunity to apply the knowledge and skills learned on the course in areas such as robot control and games development, depending on your interests.

Modules include work-based on research by our Centre for Computational Intelligence (CCI). With an established international reputation, their 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.

Reasons to study Intelligent Systems/Intelligent Systems and Robotics at DMU:

  • Artificial Intelligence is a growing industry worldwide, with a number of opportunities for further study and/or employment.
  • You will have the opportunity to choose from a range of specialist modules that will develop skills and knowledge relevant to your area of interest.
  • The course is designed to be flexible and fit around you – either choose to attend timetabled sessions on campus or distance learning; making the course suitable for recent graduates and professionals in work.
  • The CCI has an established, international reputation, with opportunities for PhD study upon successful completion of this course.
  • More than 95 per cent of recent Faculty of Technology postgraduates were in full-time employment or further education within six months of completing their course (DLHE 2013/14).
The programme consists of eight taught modules and an individual project. The only diference between the MSs IS and the MSc ISR resides in a second semester optional module: Intelligent Mobile Robots (ISR) Data Mining, Techniques and Applications (IS). More details on individual modules and MSc project provided in individual tabs on this same webpage.

Full time on-site students – study four modules in semester one, four in semester two and then work on the project over the summer period. The course takes a complete calendar year to complete in this way.

Pre-programme

Induction Unit (mid September)

Semester 1
 

Research Methods
Fuzzy Logic
Artificial Intelligence Programming
Mobile Robots

Semester 2

 

Computational Intelligence Optimisation
Artificial Neural Networks
Applied Computational Intelligence
Intelligent Mobile Robots (ISR) or Data Mining, Techniques and Applications (IS)

Summer

Masters project.

For part time on-site or distance learning students, the course can take up to a maximum of 6 years and (normally) a minimum of 2 years. One common route taken is where the taught modules are studied over a period of two years (two modules per semester, two semesters per year) and the individual project (equivalent to four modules) studied either in a third year or alongside the four modules in the second year (not recommended unless you have a lot of spare time). You need to have passed FOUR modules before you can start the project and have taken the Research Methods module.

There are variations on this – for example you could study one module per semester (so two per year), this would mean you could complete the MSc in 4 or 5 years. It is important that you discuss your plan for studying with the Programme Leader so that you are clear about your options. Do this prior to or during the induction programme at the start of the programme.

Optional Placement: We offer a great opportunity to boost your career prospects through an optional one-year placement as part of your postgraduate studies. The placement allows you to gain industrial work experience in your area of interest related to the course and increase your future employability. We have a dedicated Placements Team who will work with you to help you secure a placement. Once on your placement, you will be supported by your visiting tutor to ensure that you gain the maximum benefit from the experience.
Teaching is normally delivered through lectures, seminars, tutorials, workshops, discussions and e-learning packages. The course is divided in semesters of 15 weeks – the normal pattern will be around 10-12 lessons per module, each lesson providing approximately one week’s work. 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.

Assessment is via coursework only and will usually involve a combination of individual and group work, presentations, essays, reports and projects. Modules will include assessment of group communication activity (such as discussion board contributions or similar). The project is undertaken on an individual basis. For the project, each student is allocated a PMP (Project Management Panel) consisting of two members of staff with appropriate expertise.

Distance learning material is delivered primarily through our virtual learning environment, Blackboard (Bb). We aim to replicate the on-site experience as fully as possible by using recorded lectures and electronic discussion groups, and by encouraging contact with tutors through a variety of mediums.

The MSc may be awarded at pass, merit or distinction levels. A Postgraduate Diploma or Postgraduate Certificate may be awarded in certain cases where the award of an MSc is not appropriate.

The information provided here should be used alongside the DMU general regulations and DMU postgraduate regulations handbooks both of which can be accessed by selecting the following link: DMU Student Regulations

Semester 1: October - January (each module carries 15 credits)

IMAT5120 Research Methods

This module provides grounding in the research methods required at MSc level, looking at both quantitative and qualitative approaches including laboratory evaluation, surveys, case studies and action research. Example research studies from appropriate areas are analysed to obtain an understanding of types of research problems and applicable research methods. The research process is considered, examining how problems are selected, literature reviews, selection of research methods, data collection and analysis, development of theories and conclusions; and the dissemination of the research. Project management is studied and issues in obtaining funding and ethics are overviewed. The module exposes students to a variety of research approaches, encourages analysis of research papers and supports students in coming to conclusions concerning directions for MSc projects.
Syllabus:
  • Introduction
    • Nature and purpose of research. Overview of research process. Consideration of outcomes: publications, products, and change.
    • Analysis of Research Papers and Classification of Research.
    • Examples of research. Introduction and overview of key selected papers in appropriate areas. Analysis of papers: what is the problem? How is it tackled? Where do the authors get their data? How do they interpret it? What conclusions do they come to? What is the contribution of the paper?
    • Developing a classification of research types. Classifying the problem. Classifying the approach. Examples: Qualitative versus quantitative, positivist versus interpretive, field versus laboratory.
    • Classifying the approach to analysis: statistical, content analysis, grounded theory.
  • The Research Process
    • Defining and selecting the problem. Problem search. Motivation. Sponsors and audience. Effect of previous work. Need. Interest.
    • Reviewing previous work. The Literature review. Search and selection of sources. Evaluating and criticising previous work. Developing the story. Use of Internet sources.
    • Developing a theoretical framework. Adding to existing theory. Drawing theory from other disciplines. Developing hypotheses.
    • Selection of a research method. Relating method to problems and theory. Discussion of some available methods. Survey. Case studies. Experiments. Focus Groups. Participant Observation. Interviewing. Document analysis. Developing and evaluating a computer system. Structured evaluation studies.
    • Execution of research. Data collection. Bias. Access to organisations. Tools to support data collection. Meta-analysis. Designing computer system evaluations.
    • Analysis of research data. Overview of statistical and quantitative methods. Common statistical approaches. Dependent and independent variables. Variance. Correlation. Cronbach Alpha. Supporting and refuting hypotheses. Qualitative methods. Content analysis. Analysis of case studies.
    • Development of theories and conclusions. Extending existing theory. Developing conclusions.
    • Dissemination and presentation. Audiences. Conferences and papers. Developing the research paper. Communicating with researchers, practitioners and the public.
  • Research Support
    • Project Planning and Management. Identifying resource requirements. Planning the research project. Risk assessment.
    • Terms of Reference. Controlling the project and modifying project plans. The uncertainty of the research process.
    • Getting support. Introduction to research councils and the process of applying for a grant. Getting industrial support.
    • Ethics. Examples of projects. Are they ethical? What are the ethical issues? Involving participants.

Learning outcomes On successful completion of this module a student will be able to:
  1. Critically appraise a given research method and justification its application to appropriate research problems.
  2. Write a research proposal which demonstrates an understanding of the research process and its application to a given research problem.
  3. Identify and critically discuss professional, legal, managerial and ethical problems associated with the development and execution of a research project
Recommended Texts
Ranjit Kumar: Research Methodology: A Step-by-Step Guide for Beginners, 3rd Edition, Sage Publications Ltd, 2011.
Gerald Hall and Jo Longman (Editors): The Postgraduate's Companion, Sage Publications Ltd, 2008.
Tony Greenfield (Editor): Research Methods for Postgraduates, 2nd Edition, Arnold, 2002.

IMAT5119 Fuzzy Logic

This module provides an overview of several aspects of fuzzy logic. It provides a history of the subject and then covers in more detail the various fuzzy paradigms which have become established as useful computational tools. Applications will be discussed and students will be introduced to problem domains where problem instances may be amenable to solution by fuzzy logic techniques. Current research topics will be explored via journal and conference papers.
Topics include:
  • Historical account
  • Fuzzy Sets
  • Operations on Fuzzy Sets
  • Mamdani Inferencing
  • Sugeno Inferencing
  • Other inferencing approaches
  • ANFIS
  • Type-2 fuzzy sets
  • Operations on type-2 fuzzy sets
  • Current research issues in fuzzy logic
Learning outcomes. On successful completion of this module a student will be able to:
  1. Critically evaluate fuzzy logic approaches to solve computational problems exhibiting uncertainty and imprecision
  2. Select a problem that suits a fuzzy logic solution and implement a fuzzy logic system as a solution
  3. Have a comprehensive understanding of the successful application of fuzzy logic to several problem domains and be capable of judging whether the fuzzy paradigm might be fruitful in a novel situation.
Recommended Texts
Ross, 2007, Fuzzy Logic with engineering Applications, Wiley
Klir (1997): G. Klir, U. St. Clair, B. Yuan. Fuzzy Set Theory: Foundations and Applications, (Prentice-Hall)
Zimmerman (1991): H. J. Zimmerman. Fuzzy Set Theory and its Applications. (Kluwer Academic Publishers)
Jang (1997): J.-S. R. Jang, C.-T. Sun, E. Mitzutani Neuro-Fuzzy and Soft Computing. (Pearson Educational)
Mendel (2001): J. Mendel. Uncertain Rule-Based Fuzzy Logic Systems. (Prentice-Hall)
Klir (1988): J. Klir, T. Folger. Fuzzy Sets, Uncertainty and Information. (Prentice-Hall)

IMAT5118 AI Programming

The module presents a logical programming approach to the post-graduate programme in Computational Intelligence. AI programming is a key skill and necessary tool to appreciate and apply AI techniques for the solution of challenging problems in business and engineering. AI programming language will likely be Prolog, Lisp or some similar language for AI. The aims of the module are:
  • To develop in students the knowledge of the use of logic programming and reasoning, in order to apply AI techniques in the real world and contemplate purposeful activity in business organisations.
  • To develop in students knowledge relevant to declarative programming and predicate calculus.
  • To engender in students an understanding and appreciation of the uses of Artificial Intelligence technology to improve business management and performance.
Topics include:
  • Introduction: Declarative language and AI programming
  • Knowledge representation: First order predicate calculus
  • Search strategies: Depth first strategy and other methods.
  • Syntax of AI programs: AI programming language syntax and its code structure.
  • Unification, recursion and lists: Matching, reasoning and data management in AI programming.
  • AI programming techniques: List processing and control.
  • Application of AI programming: Expert systems, natural language processing, machine learning, game playing, qualitative reasoning and metadata programming.
Learning outcomes. On successful completion of this module a student will be able to:
  1. Critically compare knowledge representation techniques and the uses of logic reasoning in AI programming.
  2. Design and create declarative programs in an appropriate AI language.
  3. Have a critical appreciation of the role of Logic Programming in AI applications
Recommended Texts
Ivan Bratko. Prolog programming for artificial intelligence, 4th Ed. 2011 (Pearson Addison Wesley)
Michel A. Covington, Donald Nute & Andrew Vellino. Prolog programming in depth. 1997 (Prentice-Hall).
Tony Dodd. Prolog: a logical approach. 1990 (Oxford University Press)
Claudia Marcus. Prolog programming applications for database systems, expert systems, and natual language systems. 1997 (Addison-Wesley)
John Malpas. Prolog: a relational language and its applications. 1987 (Prentice-Hall)
Michael Spivey. An introduction to logic programming through prolog. 1996 (Prentice Hall)
Petter Ross. Advanced prolog.1989 (Addison-Wesley)

IMAT5121 Mobile Robots

This module covers the essentials of mobile robots. It initiates analytical discussion of the hardware and software architectures used to build real-world mobile robot systems. It introduces all the necessary topics required to enable students to develop software to create intelligent autonomous robots, including: low-level programming of I/O devices, sensor systems, and artificial intelligence. The major part of the course is project based with a grand challenge issued to the students, e.g to solve a maze or to follow an obstacle course.
Topics include:
  • Introduction to mobile robotics: Definitions, foundations of mobile robotics research, early examples. Current implementations, applications and research issues.
  • Sensors and actuators: Physical principles of sensors and actuators, sensor signal processing, sensor data interpretation.
  • Real-Time Programming: Introduction to low-level programming in C/C++. Polling and interrupts. Digital and Analogue I/O and interfacing requirements. Concurrency.
  • Control Systems: Introduction to feedback control,binary control, hystersis, open loop and PID control contextualised in robot motor control.
  • Intelligent Robots: Reactive, model based and hybrid control architectures. Introduction to reinforcement learning, planning and robot collaboration.
Learning outcomes. On successful completion of this module a student will be able to:
  1. Demonstrate a comprehensive understanding of the principles and techniques used in building and controlling autonomous mobile robots by the design and implementation of adaptable controllers for autonomous mobile robots on a real robot system.
  2. Demonstrate a comprehensive understanding of the theoretical principles of the techniques used in building and controlling autonomous mobile robots and of the advances that are being made in this field.
Recommended Text
The Robotics Primer (Intelligent Robotics & Autonomous Agents) (Intelligent Robotics & Autonomous Agents Series) by Maja J Mataric.

Semester 2: February - May (each module carries 15 credits)

IMAT5232 Computational Intelligence Optimisation

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. This subject contains algorithmic structure based on metaphors such as evolution and collective intelligence. This module will provide students with an appreciation of both, theoretical and implementation issues of CIO algorithms. Selected algorithms (negotiated between the lecturer and each student) will be studied in practical work.
Outline content:
  • Generalities: Definition and postulate of optimisation problems, fitness landscape and problem features, No Free Lunch Theorem
  • Classical derivative free methods: General concepts of Rosenbrock, Hooke-Jeeves, Nelder-Mead Algorithms, Simulated Annealing, Multi-start search
  • Popular population-based algorithms: Genetic Algorithms, Evolution Strategy, other examples of modern evolutionary approaches, Particle Swarm Optimisation
  • Differential Evolution, other examples of modern swarm intelligence algorithms, perturbation mechanisms of an algorithmic scheme
  • An overview on modern approaches: Adaptive Systems, Hyper-heuristics, Memetic Computing
  • An overview on special problems: Multi-objective, noisy, dynamic, computationally expensive, and large scale problems
  • Application examples: control theory, image processing
Learning outcomes. On successful completion of this module a student will be able to:
  1. Demonstrate a comprehensive understanding of Computational Intelligence Optimisation;
  2. Be able to critically apply and implement the taught CIO algorithms to given test problems.
Recommended Texts
Handbook of Memetic Algorithms F. Neri, C. Cotta, P. Moscato by Springer
Introduction to Evolutionary Computing A. E. Eiben, J. Smith by Springer
Introduction to Genetic Algorithms Sivanandam, S.N.; Deepa, S.N. Published By: Springer

IMAT5235 Artificial Neural Networks

This module provides a detailed appraisal of several aspects of neural network computing. It provides a history of the subject and then covers in detail the various network paradigms which have become established as useful computational tools. Applications will be discussed and students will be introduced to problem domains where problem instances may be amenable to solution by neural network techniques. Whilst the module will concentrate on an Engineering approach there will also be discussion of the use of networks for cognitive modelling.
Topics include:
  • Historical account
  • Learning paradigms
  • Feed Forward Networks (Classical and Modern approaches)
  • Self-organising maps
  • Recurrent Networks
  • Applications to: pattern recognition; classification problems; data modelling; time series; cognitive modelling
Learning outcomes. On successful completion of this module a student will be able to:
  1. Apply modelling approaches which use neural networks to solve computational problems;
  2. Implement a variety of network solutions;
  3. Have a comprehensive knowledge of the successful application of neural networks to several problem domains and be capable of judging whether the neural computational approach might be fruitful in a novel situation;
  4. Participate in the peer review process.
Recommended Text
Steeb, 2011, The Non-Linear Workbook, available on Amazon.

IMAT5234 Applied Computational Intelligence

The purpose of this module is to enable students to appreciate the historical, philosophical and future implications of AI in relation to both theoretical and practical aspects and to investigate at least one application area in depth.
Topics include:
  • History & philosopshy of AI: history of developments in AI; exploration of prevalent philosophical views and theories e.g. Searle, Dennett, Penrose;
  • Expert Systems: Knowledge acquisition, representation and search, expert system shells.
  • Applications: an exploration of applications in a variety of different areas will be achieved by combinations of study of current research papers, guest speakers, tutors’ own research & the investigative work of the students within the module.
Learning outcomes. On successful completion of this module a student will be able to:
  1. Apply AI techniques to given practical problems
  2. Recognise the multi-disciplinary nature of AI and its potential application areas.
  3. Critically appraise relevant literature in order to formulate a plan for their own practical/experimental work
  4. Synthesise a solution to a problem (planned in LO3) and evaluate the solution
Recommended Text
Intelligent Systems for Engineers & Scientists, Hopgood, CRC Press, 2001 (the newer edition may be available now)

IMAT5233 Intelligent Mobile Robots (MSc ISR)

This module builds on the material covered in Mobile Robots to provide a comprehensive understanding of autonomous mobile robots and autonomous navigation. The aim of the course is to enable the student to comprehend and argue constructively the space, reasoning and navigation. In this module students will be required to analyse, evaluate and construct odometry systems, maps, navigation plans and localisation techniques for mobile robots. Issues related to the sensing, representing and modelling of the environment will be assessed. Some algorithmic solutions will be synthesised and assessed. Advanced issues such as simultaneous localisation and mapping will be critically discussed.
Topics include:
  • Representational issues and reasoning representing space, the robot and action representing knowledge and perception path-planning
  • Localisation
  • Mapping unknown environments
  • Handling sensory uncertainties using stochastic methods
  • Applying particle filters to localisation
  • Having some understanding of how Kalman filters can be applied to the problem of simultaneous localisation and mapping
Learning outcomes. On successful completion of this module a student will be able to:
  1. Demonstrate a comprehensive understanding of the various approaches to incorporating spacial awareness and navigation in mobile robot behaviour.
  2. Demonstrate the ability to critically evaluate navigation and localisation solutions.
Recommended Text
Murphy, 2000, An Introduction to AI Robotics, MIT Press

IMAT5238-2017-2 Data Mining, Techniques and Application (MSc IS)

Data is collected and stored in all different types of organisations - commercial, governmental, educational. Every day hundreds of terabytes of data are circulated via the Internet. Organisations are now making the most out of the data they electronically capture by looking for hidden patterns and extracting meaningful information for decision making purposes. For example, in marketing, customers who are most likely to buy certain products and services should be targeted. In fraud detection, it is of interest to investigate unusual behaviour patterns to identify insurance claims, cellular phone calls and credit card purchases that are most likely to be fraudulent. Data mining is a collection of tools, methods and statistical techniques for exploring and modelling relationships in large amounts of data, to enable meaningful information to be extracted for decision making purposes. The aim of this module is to review the data mining methods and techniques available for uncovering important information from large data sets and to know when and how to use a particular technique effectively. The module will enable the student to develop an in-depth knowledge of applying data mining methods and techniques and interpreting the statistical results in relevant problem domains. Current application areas and research topics in data mining will also be discussed and students will be expected to contribute to these discussions to increase their background knowledge and understanding of issues and developments associated with data mining. The module uses the data mining tool SAS Enterprise Miner.
Topics include:
  • Introduction to data mining
  • Data mining methodology
  • Exploratory data analysis including association analysis
  • Cluster analysis in data mining
  • Predictive modelling using Regression
  • Predictive modelling using Decision Trees
  • Predictive modelling using Neural Networks
  • Model evaluation: Comparing Candidate Models
  • Model implementation: Generating and Using Score Code
  • Current research and application in data mining
Learning outcomes. On successful completion of this module a student will be able to:
  1. Have an in-depth understanding of the key concepts of data mining
  2. Appreciate the breadth of areas of application and research in data mining
  3. Systematically apply appropriate data mining methods and techniques for particular problem domains
  4. Correctly interpret and critically evaluate the results to make informed decisions
Recommended Texts
Berry Michael and Linoff Gordon (2011). Data Mining Techniques. 3rd ed. ISBN 978-0-470-65093-6 (Wiley)
SAS Publishing (2003): Data Mining using SAS Enterprise Miner: A Case Study Approach. 2nd ed. ISBN 1-59047-190-3 (SAS Publishing.)
Baker Stephen (2009). They've got your number: Data, Digits and Destiny - how the Numerati are changing our lives.

Summer: June - August (60 credits; part-time students usually have one year for the project)

The project forms an important element of the MSc course, and must be passed to obtain the degree. Further, the project must be passed at distinction level before an overall MSc with distinction award will be made, and similarly must be passed at merit level before an overall MSc with merit award will be made.

The aim of the project is to provide the student with the opportunity to carry out an in-depth study involving critical analysis, and to demonstrate the application of skills acquired from the taught component of the course, to the solution of a particular problem. The project should be a self-contained piece of work of considerably greater depth than can be accommodated within a taught module. It should include a substantial element of scholarly research and fact-finding so that (a) it demonstrates research and analysis skills appropriate to a masters degree, and (b) the creative work of the project is based on a solid foundation of knowledge and conceptual understanding of the problem. The postgraduate nature of the project should be evident from the higher overall standard compared to an undergraduate project, in the depth of critical analysis, the insight required and the complexity of the task undertaken. Students will be expected to demonstrate project management and presentation skills throughout the period of the project when liaising with their Supervisors and Project Management Panels (PMPs).

The viva voce examination is a mandatory component of the module – not having a viva counts as a non-submission of the project. The Student arranges a time for it that suits the Supervisor and Second Marker. This is normally after the submission deadline and in time for marking to be completed and marks to be processed and the degree awarded at the next Postgraduate Assessment Board.

Students are responsible for investigating possible projects and discussing these with the Supervisor, and where possible the proposer and other stakeholders, and getting a clear agreement on the project with the Supervisor. Full time students are strongly advised to do this before starting full-time work on their projects after their second semester exams.

Full-time students are expected to devote all their time (notionally 40 hours per week) to the project on completion of the Semester 2 examinations