|Module Title||ADVANCED ARTIFICIAL INTELLIGENCE CONCEPTS|
|Co-ordinator||Dr Mark Ratcliffe|
|Course delivery||Lecture||20 lectures|
|Seminars / Tutorials||(Up to) 11 workshop sessions|
|Supplementary examination||Will take the same form, under the terms of the Department's policy.|
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.
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.
** 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