School of Computer Science

Module 12412 (2010)

Module description - Introduction to Neural Computation

The Module Description is a strict subset of the Syllabus Page.

Module Title Introduction to Neural Computation
School School of Computer Science
Module Code 06-12412
Level 4/M
Member of Staff John Bullinaria
Semester Semester 1 - 10 credits
Delivery

2 hrs/week of lectures, assigned course work

Outcomes

On successful completion of this module, the student should be able to:

  • Understand the relationship between real brains and simple artificial neural network models
  • Describe and explain some of the principal architectures and learning algorithms of neural computation
  • Explain the learning and generalisation aspects of neural computation
  • Apply neural computation algorithms to specific technical and scientific problems
  • Demonstrate an understanding of the benefits and limitations of neural-based learning techniques in context of other state-of-the-art methods of automated learning
Assessment
  • Sessional: 1.5 hr examination (80%), continuous assessment (20%)
  • Supplementary: 1.5 hr examination (80%), continuous assessment (20%)
Texts
TitleAuthorPublisher
An Introduction to Neural Networks Kevin Gurney CRC Press
Fundamentals of Neural Networks L Fausett Prentice Hall
Introduction to Neural Networks R Beale & T Jackson IOP Publishing
Introduction To The Theory Of Neural Computation John A. Hertz; Anders S. Krogh; Richard G. Palmer Westview Press
Neural Networks: A Comprehensive Foundation (Second Edition) S Haykin Prentice Hall
Neural Networks for Pattern Recognition C M Bishop Oxford University Press
Principles of Neurocomputing for Science and Engineering F M Ham & I Kostanic McGraw Hill
The Essence of Neural Networks R Callan Prentice Hall Europe