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School of Engineering and Informatics (for staff and students)

Cybernetics and Neural Networks (100H6)

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Cybernetics and Neural Networks

Module 100H6

Module details for 2024/25.

15 credits

FHEQ Level 7 (Masters)

Module Outline

This is an introductory course that lays the foundations for further self study, many of the illustrations have been simplified to demonstrate principles to facilitate understanding. Emphasis is placed on analysis of basic neural network architectures and learning rules. The course spends significant time exploring training of neural networks. The utilisation of artificial intelligence techniques in neural networks is explored.
Software implementation of theoretical concepts will solve genuine engineering problems in dynamic feedback control systems, pattern recognition and scheduling problems. In many instances solutions must be computed in response to data arriving in real-time (e.g. video data). The implications of high speed decision making will be included. Engineering design skills, programming skills in a high level language.

The syllabus covers the following AHEP4 learning outcomes:

M1, M2, M3, M4, M5, M6, M12

Library

Martin T. Hagan, "Neural Network Design", PWS Publishing Company, ISBN 0-534-94332-2, 1996, QA76.87.H34
Alison Cawsey, "The Essence of Artificial Intelligence", Prentice Hall, ISBN 0-13-571779-5, 1998, QZ1250 Caw
S. Haykin "Neural Networks: A comprehesive Foundation", MacMillan, ISBN 0-13-273350-1, 1999, QZ 1335 Hay
Howard L. Resnikoff "The Illusion of Reality", Springer-Verlag, ISBN 0-387-96398-7, 1989, QE 1300 Res
A. White, A Sofge "Handbook of Intelligent Control: Neural, Fuzzy and Adaptive Approaches" Van Nostrand Reihold, 1992, QZ 1275 Han

Module learning outcomes

Understand how neural networks can be applied to solve a range of engineering and data processing problems.

Have an understanding of the different classes of neural networks.

Understand the importance and difficulty of training neural networks.

Have an overview of the application of neural networks to artificial intelligence (AI).

TypeTimingWeighting
Computer Based ExamSemester 1 Assessment80.00%
Coursework20.00%
Coursework components. Weighted as shown below.
ReportT1 Week 11 100.00%
Timing

Submission deadlines may vary for different types of assignment/groups of students.

Weighting

Coursework components (if listed) total 100% of the overall coursework weighting value.

TermMethodDurationWeek pattern
Autumn SemesterLaboratory1 hour00111111000
Autumn SemesterLecture2 hours11111111111

How to read the week pattern

The numbers indicate the weeks of the term and how many events take place each week.

Prof Chris Chatwin

Assess convenor
/profiles/9815

Please note that the University will use all reasonable endeavours to deliver courses and modules in accordance with the descriptions set out here. However, the University keeps its courses and modules under review with the aim of enhancing quality. Some changes may therefore be made to the form or content of courses or modules shown as part of the normal process of curriculum management.

The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.

School of Engineering and Informatics (for staff and students)

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