Module Information

Module Identifier
Module Title
Academic Year
Semester 1
CS26110 equivalent.
Other Staff

Course Delivery

Delivery Type Delivery length / details
Lecture 20 lectures


Assessment Type Assessment length / details Proportion
Semester Exam 2 Hours   Written Exam  100%
Supplementary Exam 2 Hours   Resit failed examination  100%

Learning Outcomes

On successful completion of this module, students will be able to:
1. Formalise and represent experience, using techniques like feature-value pairs;
2. Apply concept learning, decision making tree, artificial neural networks, Bayesian learning, genetic algorithms, and reinforcement learning to solve a range of simple problems in applications like customer attitude prediction, banking business, handwritten character recognition, medical diagnosis, artificial life, robot path optimization, etc.;
3. Compare and choose different learning algorithms for different applications;
4. Appreciate the range of applicability of intelligent learning concepts and techniques; ;
5. Explain the state of the art of intelligent learning concepts and techniques;

Brief description

This module builds on CS26110 and is application oriented. To this end, a small number of topics are studied in depth in order to give insight and understanding of the methods and issues involved in the state-of-the-art applications of the various learning techniques.


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

This chapter will use concept learning to develop applications to predict whether students like this module or not: Generality ordering of hypotheses, FIND-S algorithm, version space, the LIST-THEN-ELIMINATE algorithm, inductive bias

3. Decision tree learning - 3 lectures

This chapter will use decision tree learning to develop applications about banking business: 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

This chapter will use artificial neural network learning to develop applications to recognize handwritten characters: Perceptron, linear separability, gradient decent, sigmoid function, back propagation algorithm, over-fitting.

5. Bayesian learning - 4 lectures

This chapter will use Bayesian learning to develop applications about medical diagnosis: Bayesian theory, maximum a posteriori hypothesis, maximum likelihood, probability density, normal distribution, minimum description length principle, Bayes optimal classifier, naive Bayes classifier.

6. Support vector machines - 3 lectures

This chapter will introduce the ideas, formulations and applications of support vector machines: Binary linear classifier, maximum margin hyperplane, linear separability/non-linear separability, kernel function, hard/soft margin, primal/dual form, quadratic programming, cross validation, applications.

7. Reinforcement learning - 2 lectures

This chapter will use reinforcement learning to develop applications about robot path optimization: Reward, Markov decision process, utility function, Q-learning.

Reading List

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


This module is at CQFW Level 6