Module 20416 (2011)

Syllabus page 2011/2012

Neural Computation

Level 3/H

John Bullinaria
10 credits in Semester 1

Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus

The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)

Relevant Links

See the Module Web-page for further information.


This module introduces the basic concepts and techniques of neural computation, and its relation to automated learning in computing machines more generally. It covers the main types of formal neuron and their relation to neurobiology, showing how to construct large neural networks and study their learning and generalization abilities in the context of practical applications.


The aims of this module are to:

  • Introduce some of the fundamental techniques and principles of neural computation
  • Investigate some common neural-based models and their applications
  • Present neural network models in the larger context of state-of-the-art techniques of automated learning

Learning Outcomes

On successful completion of this module, the student should be able to: Assessed by:
1understand the relationship between real brains and simple artificial neural network models Examination
2describe and explain some of the principal architectures and learning algorithms of neural computation Examination
3explain the learning and generalisation aspects of neural computation Examination
4demonstrate an understanding of the benefits and limitations of neural-based learning techniques in context of other state-of-the-art methods of automated learning Examination

Restrictions, Prerequisites and Corequisites


May not be taken by anyone who has taken or is taking 06-12412 (Introduction to Neural Computation).






Teaching Methods:

2 hrs/week of lectures

Contact Hours:



  • Sessional: 1.5 hr examination (100%)
  • Supplementary (where allowed): As the normal assessment.

Recommended Books

TitleAuthor(s)Publisher, Date
Neural Networks: A Comprehensive Foundation (Second Edition)S HaykinPrentice Hall, 1999
Neural Networks for Pattern RecognitionC M BishopOxford University Press, 1995
The Essence of Neural NetworksR CallanPrentice Hall Europe, 1999
An Introduction to Neural NetworksK GurneyRoutledge, 1997
Fundamentals of Neural NetworksL FausettPrentice Hall, 1994
Introduction to Neural NetworksR Beale & T JacksonIOP Publishing, 1990
An Introduction to the Theory of Neural ComputationJ Hertz, A Krogh & R G PalmerAddison Wesley, 1991
Principles of Neurocomputing for Science and EngineeringF M Ham & I KostanicMcGraw Hill, 2001

Detailed Syllabus

  1. Introduction to Neural Networks and their History
  2. Biological Neurons and Neural Networks, Artificial Neurons
  3. Networks of Artificial Neurons, Single Layer Perceptrons
  4. Learning and Generalization in Single Layer Perceptrons
  5. Hebbian Learning, Gradient Descent Learning
  6. The Generalized Delta Rule, Practical Considerations
  7. Learning in Multi-Layer Perceptrons - Back-Propagation
  8. Learning with Momentum, Conjugate Gradient Learning
  9. Bias and Variance - Under-Fitting and Over-Fitting
  10. Improving Generalization
  11. Applications of MLPs
  12. Recurrent Neural Networks
  13. Radial Basis Function Networks: Introduction
  14. Radial Basis Function Networks: Algorithms
  15. Radial Basis Function Networks: Applications
  16. Self Organizing Maps: Fundamentals
  17. Self Organizing Maps: Algorithms and Applications
  18. Learning Vector Quantization
  19. Committee Machines
  20. Mixture Models

Last updated: 24 September 2008

Source file: /internal/modules/COMSCI/2011/xml/20416.xml

Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus