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Machine Learning and Computational Intelligence - ELE00162M

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  • Department: Electronic Engineering
  • Module co-ordinator: Prof. Stephen Smith
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2024-25
    • See module specification for other years: 2023-24

Module summary

This module will introduce the theory of a range of machine learning and computational intelligence methods including supervised, unsupervised and reinforcement learning, automata and other learning systems, develop practical skills in relevant software tools and systems, and explore their application through real-world practical examples.

Module will run

Occurrence Teaching period
A Semester 1 2024-25

Module aims

  • Introduce the theory range of machine learning and computational intelligence methods including supervised, unsupervised and reinforcement learning, automata and other learning systems

  • Develop practical skills in relevant software tools and systems

  • Application through real-world practical examples

Module learning outcomes

  • Be able to discuss the principles of contemporary machine and computational intelligence technologies

  • Practical experience in using relevant software tools and systems

  • Problem solving and inference using tools and systems applied to real-world problems/applications

Module content

  • Introduction to machine learning and computational intelligence including neural, statistical, and swarm intelligent methodologies

  • Supervised learning systems, e.g. neural networks, decision trees, genetic programming

  • Unsupervised learning systems. e.g. K-means, autoencoders and other clustering approaches

  • Reinforcement learning including Q-learning and reward-based mechanisms

  • Probabilistic learning including Bayesian networks, Kalman filters, and Hidden Markov Models

  • Future methods

Assessment

Task Length % of module mark
Essay/coursework
Mini-project and report		 N/A 100

Special assessment rules

None

Reassessment

None

Module feedback

'Feedback’ at a university level can be understood as any part of the learning process which is designed to guide your progress through your degree programme. We aim to help you reflect on your own learning and help you feel more clear about your progress through clarifying what is expected of you in both formative and summative assessments. A comprehensive guide to feedback and to forms of feedback is available in the Guide to Assessment Standards, Marking and Feedback.

The School of PET aims to provide some form of feedback on all formative and summative assessments that are carried out during the degree programme. In general, feedback on any written work/assignments undertaken will be sufficient so as to indicate the nature of the changes needed in order to improve the work. The School will endeavour to return all exam feedback within the timescale set out in the University's Policy on Assessment Feedback Turnaround Time. The School would normally expect to adhere to the times given, however, it is possible that exceptional circumstances may delay feedback. The School will endeavour to keep such delays to a minimum. Please note that any marks released are subject to ratification by the Board of Examiners and Senate. Meetings at the start/end of each term provide you with an opportunity to discuss and reflect with your supervisor on your overall performance to date.

Indicative reading

TBC


The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.