School of Computer Science

Module 06-20416 (2011)

Neural Computation

Level 3/H

John Bullinaria Semester 1 10 credits
Co-ordinator: John Bullinaria
Reviewer: Peter Tino

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

Aims

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:

  • 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
  • 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

Teaching methods

2 hrs/week of lectures


Assessment

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

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

Programmes containing this module