Cybernetics and Neural Networks (100H6)
Cybernetics and Neural Networks
Module 100H6
Module details for 2022/23.
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).
| Type | Timing | Weighting |
|---|---|---|
| Computer Based Exam | Semester 1 Assessment | 80.00% |
| Coursework | 20.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | T1 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.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Autumn Semester | Laboratory | 1 hour | 00111111000 |
| Autumn Semester | Lecture | 2 hours | 11111111111 |
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
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