Module Identifier CS36110  
Academic Year 2003/2004  
Co-ordinator Dr Mark B Ratcliffe  
Semester Semester 1  
Other staff Dr Yonghuai Liu  
Pre-Requisite CS26210  
Course delivery Lecture   20 lectures  
Assessment TypeAssessment Length/DetailsProportion
Semester Exam2 Hours  100%
Supplementary Exam Will take the same form, under the terms of the Department's policy.   
Further details  

Learning outcomes

On successful completion of this module, students will be able to:
1. appreciate the range of applicability of intelligent learning concepts and techniques;
2. explain the state of the art of intelligent learning concepts and techniques;
3. have sufficient knowledge of a basic rule induction method, such as ID3, to be able to explain it;
4. explain the theory behind artificial neural networks, in particular perceptron and back propagation;
5. describe and explain the theory behind concept learning, Baysian learning, genetic algorithm, reinforcement learning, and inductive logic programming;
6. apply Baysian theory to justify concept learning, decision tree learning, artificial neural network learning.

Brief description

This module builds on CS26210 and examines some of the key ideas in Artificial Intelligence. A small number of topics are studied in depth in order to give insight and understanding of the methods and issues involved in state-of-the-art developments.


1. Introduction - 2 lectures
Possibility and necessity of learning, target function,components in a learning system, performance measurement of learning systems.

2. Concept learning - 3 lectures
Generality ordering of hypotheses, FIND-S algorithm, candidate elimination algorithm, version space, the LIST-THEN-ELIMINATE algorithm, inductive bias

3. Decision tree learning - 3 lectures
Entropy, best attribute, information gain, best tree, inductive bias, Occam's razor, over-fitting, reduced error pruning, rule post-pruning

4. Artificial neural network - 3 lectures
Perceptron, linear separability, gradient decent, sigmoid function, back propagation algorithm, over-fitting

5. Baysian learning - 4 lectures
Baysian theory, maximum a posteriori hypothesis, maximum likelihood, probability density, normal distribution, minimum description length principle, Bayes optimal classifier, naive Bayes classifier

6. Genetic algorithm - 3 lectures
Best hypothesis, hypothesis representation, genetic operators, fitness function, fitness proportionate selection, steady state selection, rank based selection

7. Reinforcement learning - 2 lectures
Q-learning, adaptive dynamic programming, temporal difference learning.

Reading Lists

** Recommended Text
T. Mitchell (1998) Machine Learning McGraw Hill
S. M. Weiss and C. A. Kulikowsky (1991) Computer Systems That Learn Morgan Kaufmann
S J Russell and P Norvig (1995) A I: A Modern Approach Prentice-Hall ISBN 0131038052


This module is at CQFW Level 6