Module Identifier CS36110  
Academic Year 2000/2001  
Co-ordinator Dr Mark Ratcliffe  
Semester Semester 1  
Pre-Requisite CS26210  
Course delivery Lecture   20 lectures  
  Seminars / Tutorials   (Up to) 11 workshop sessions  
Assessment Exam   2 Hours   100%  
  Supplementary examination   Will take the same form, under the terms of the Department's policy.    

General 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.

The primary aim of this module is to give students a deeper understanding of some of the principle concepts of artificial intelligence, developed in the Artificial Intelligence Concepts module CS26210 . In concentrating on model-based reasoning and machine learning, this module will make full use of the expertise and research strengths of members of the lecturing staff.

Learning outcomes
On successful completion of this module students should:

1. Model Based Reasoning - 8 Lectures
This part of the module begins with the basic concepts of model-based reasoning, and proceeds to cover types of model, qualitative reasoning, and applications.

2. Machine Learning - 12 Lectures
A detailed view of the central problems of machine learning, and overview of the most important current techniques: decision trees, neural networks, genetic algorithms and inductive logic programming.

Reading Lists
** Recommended Text
T. Mitchell. (1987) Machine Learning. McGraw Hill
B.J. Kuipers. (1994) Qualitative reasoning: modelling and simulation with incomplete knowledge. MIT Press
** Consult For Futher Information
S. M. Weiss and C. A. Kulikowsky. (1991) Computer Systems That Learn. Morgan Kaufmann