Teaching -> Neural Networks
Duration: Approximately 30 lecture hours
Duration: Approximately 30 lecture hours
Prerequisites: Knowledge in Calculus, Linear Algebra and Matlab
Course Contents:
- Introduction
- Neuron Models and Network Architectures
- Perceptron Learning Rule
- Supervised Hebbian Learning
- Widrow-Hoff Learning
- Back Propogation
- Associative Learning
- Competitive Networks
Objectives:
- To introduce the main concepts, techniques and applications in the field of Neural Networks.
Learning outcomes:
- Have an understanding of the concepts and techniques of neural networks through the study of the most important neural network models.
- Have a knowledge of sufficient theoretical background to be able to reason about the behaviour of neural networks.
- Be able to evaluate whether neural networks are appropriate to a particular application.
- Be able to apply neural networks to particular applications, and to know what steps to take to improve performance.
- Be able to implement Neural Network algorithms in Matlab.
Method of Assessments: Explained in the class
Attendance: Only those who satisfy the requirement of attendance (at least 80%) at lectures are allowed to sit the end of semester examination.
Recommended Reading:
- M. Hagan, H. Demuth and M. Beale, Neural Network Design, PWS Publishing Company, 1996.
- H. Demuth and M. Beale, Neural Network Toolbox For Use With Matlab, User's Guide, Version 4, The Mathworks Inc., July 2002.
