Accessibility statement

Neural Networks & Neural Computing - ELE00115M

« Back to module search

  • Department: Electronic Engineering
  • Module co-ordinator: Dr. David Halliday
  • Credit value: 10 credits
  • Credit level: M
  • Academic year of delivery: 2022-23
    • See module specification for other years: 2021-22

Module summary

Artificial Intelligence is one of the most important and influential research field of the modern world. This module introduces you to the biologically inspired methods of processing information in Artificial Neural Networks. The implications for information searching and management, robotics, and major international networks are enormous. You will understand how perceptrons work, and gain experience in the structure and operation of a wide variety of techniques in neural networks and computing.

Module will run

Occurrence Teaching period
A Spring Term 2022-23

Module aims

Subject content aims:

  • To provide an introduction to the field of Neural Networks and Neural Computing.

Graduate skills aims:

  • To appreciate the different types of neural network technologies and application areas.

Module learning outcomes

Subject content learning outcomes

After successful completion of this module, students will:

  • Demonstrate an understanding of the structure and function of a neural computing unit.

  • Demonstrate an understanding of the following types of artificial neural networks: Perceptron, Multilayer perceptron, Deep, Associative, Hebbian, Competitive and Boltzmann networks.

  • Demonstrate an understanding of the range of learning rules applied to these networks, including: Widrow-Hoff, Back-propagation, Hebbian, Oja, Sanger, Competitive, Kohonen and Boltzmann rules.

  • Demonstrate an understanding of Spiking Neural Networks.

  • Calculate and use the Widrow-Hoff delta rule for Perceptron networks and the Hopfield energy function for associative networks.

Graduate skills learning outcomes

After successful completion of this module, students will:

  • Communicate understanding and design choices through written content.

  • Use MATLAB to implement and apply a range of neural network algorithms.

  • Apply theoretical knowledge of neural networks to solve problems.

Assessment

Task Length % of module mark
Essay/coursework
Neural Networks & Neural Computing		 N/A 100

Special assessment rules

None

Reassessment

Task Length % of module mark
Essay/coursework
Neural Networks & Neural Computing		 N/A 100

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. This can be found at https://www.york.ac.uk/students/studying/assessment-and-examination/guide-to-assessment/ The Department of Electronic Engineering 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. Students are provided with their examination results within 20 working days of the end of any given examination period. The Department will also endeavour to return all coursework feedback within 20 working days of the submission deadline. The Department would normally expect to adhere to the times given, however, it is possible that exceptional circumstances may delay feedback. The Department 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

+ Churchland, PS, & Sejnowski, TJ, 'The Computational Brain', MIT Press, 1992. ISBN 0-262-53120-8. JBM Library: SK 70 HER, 5 Copies.

++ Hertz, J, Krogh, A, & Palmer, RG, 'Introduction to the Theory of Neural Computation', Addison Wesley, 1991 ISBN 0-201-50395-6. JBM Library: B 3.11 CHU, 6 Copies.


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.