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 Natural Language Processing Based on Deep Learning

This module 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 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.

It provides practical experience of using the theoretical underpinning to facilitates engagement in solving complex applications in natural language processing.

Learning Objectives for the Module

Upon successful completion of the module you will be able to:

1. Develop in students an in depth 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.

2. Develop in students advanced knowledge relevant to declarative programming and predicate calculus.

3. Have a comprehensive understanding and appreciation of the uses of Artificial Intelligence technology to improve business management and performance.

Recommended Texts

Natural Language Processing with Python. Steven Bird, Ewan Klein and

Edward Loper. O’Really. 2009.

Language Processing with PyTorch. Delip Rao and Brian

McMahon. O’Really. 2019.

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.