Module 20416 (2008)
Syllabus page 2008/2009
06-20416
Neural Computation
Level 3/H
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.
Outline
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.
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: | Assessed by: | |
| 1 | understand the relationship between real brains and simple artificial neural network models | Examination |
| 2 | describe and explain some of the principal architectures and learning algorithms of neural computation | Examination |
| 3 | explain the learning and generalisation aspects of neural computation | Examination |
| 4 | 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 | Examination |
Restrictions, Prerequisites and Corequisites
Restrictions:
May not be taken by anyone who has taken or is taking 06-12412 (Introduction to Neural Computation).
Prerequisites:
None
Co-requisites:
None
Teaching
Teaching Methods:
2 hrs/week of lectures
Contact Hours:
Assessment
- Sessional: 1.5 hr examination (100%)
- Supplementary (where allowed): As the normal assessment.
Recommended Books
| Title | Author(s) | Publisher, Date |
| Neural Networks: A Comprehensive Foundation (Second Edition) | S Haykin | Prentice Hall, 1999 |
| Neural Networks for Pattern Recognition | C M Bishop | Oxford University Press, 1995 |
| The Essence of Neural Networks | R Callan | Prentice Hall Europe, 1999 |
| An Introduction to Neural Networks | K Gurney | Routledge, 1997 |
| Fundamentals of Neural Networks | L Fausett | Prentice Hall, 1994 |
| Introduction to Neural Networks | R Beale & T Jackson | IOP Publishing, 1990 |
| An Introduction to the Theory of Neural Computation | J Hertz, A Krogh & R G Palmer | Addison Wesley, 1991 |
| Principles of Neurocomputing for Science and Engineering | F M Ham & I Kostanic | McGraw Hill, 2001 |
Detailed Syllabus
- Introduction to Neural Networks and their History
- Biological Neurons and Neural Networks, Artificial Neurons
- Networks of Artificial Neurons, Single Layer Perceptrons
- Learning and Generalization in Single Layer Perceptrons
- Hebbian Learning, Gradient Descent Learning
- The Generalized Delta Rule, Practical Considerations
- Learning in Multi-Layer Perceptrons - Back-Propagation
- Learning with Momentum, Conjugate Gradient Learning
- Bias and Variance - Under-Fitting and Over-Fitting
- Improving Generalization
- Applications of MLPs
- Recurrent Neural Networks
- Radial Basis Function Networks: Introduction
- Radial Basis Function Networks: Algorithms
- Radial Basis Function Networks: Applications
- Self Organizing Maps: Fundamentals
- Self Organizing Maps: Algorithms and Applications
- Learning Vector Quantization
- Committee Machines
- Mixture Models
Last updated: 24 September 2008
Source file: /internal/modules/COMSCI/2008/xml/20416.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus