Module Identifier | CS36310 | |||||||||||

Module Title | ARTIFICIAL INTELLIGENCE SYSTEMS | |||||||||||

Academic Year | 2004/2005 | |||||||||||

Co-ordinator | Dr Adrian D Shaw | |||||||||||

Semester | Semester 2 | |||||||||||

Other staff | Dr Adrian D Shaw, Professor Mark H Lee | |||||||||||

Pre-Requisite | CS26210 | |||||||||||

Course delivery | Seminars / Tutorials | Up to 2 workshop sessions | ||||||||||

Practical | Up to 1 session | |||||||||||

Lecture | 20 lectures | |||||||||||

Assessment |
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Further details | http://www.aber.ac.uk/compsci/ModuleInfo/CS36310 |

- understand the differences between the main classes of application oriented intelligent system, including representation and reasoning facilitites, and performance issues;
- understand the issues involved in constructing effective diagnostic systems;
- appreciate the problem of software engineering for intelligent systems;
- have familiarity with current machine learning and data mining techniques and tools;
- be able to assess the applicability of Bayesian probability theory to realistic data analysis problems;
- be able to apply current data mining techniques to real world problems;
- be capable of avoiding overfitting whe using data mining tools.

Building on CS36210, these lectures will introduce how to build effective diagnostic systems for man made systems. It will therefore look at diagnostic trees, diagnostic test benches and issues associated with diagnostic systems. From these lectures the student should gain an understanding of the features of commercial expert systems tools and their strengths and limitations.

2. Case based reasoning - 4 Lectures

These lectures will provide the student with an understanding of the techniques which comprise a case-based reasoning (CBR) system. They should also gain an understanding of their strengths and weaknesses for diagnostic applications as well as when to apply and when not to apply CBR. The lectures will consider the basic structure of a CBR system and the use of a case base, representation and indexing of cases, case retrieval and adaptation and finally case repair.

3. Model-based Reasoning - 1 Lectures

In these lectures "Functional" model based reasoning will be introduced. How a functional model-based system can be constructed and the problems and issues associated with such a task will be considered. In addition a functional model-based system constructed for Engineers will be considered in detail. This case study will highlight the trade offs which had to be made between ease of use and flexibility, power, etc.

4. MBR for Failure Mode Effect Analysis - 1 Lectures

These lectures will look at how MBR can be used as the basis of a design analysis system which can be used to provide information necessary for the construction of diagnostic systems.

5. General data analysis - 5 lectures

Probability theory, survey of data analysis methods, overfitting, cross-validation, ensemble learning.

6. Neural Networks - 2 Lectures

These lectures will introduce the concepts behind Neural Networks and investigate some of the more important and useful neural network architectures. They will also aim to give an appreciation of the application of neural computing in industry.

7. Machine Induction - 2 Lectures

There are a large number of applications which exploit the approach to machine learning known as induction. In these lectures we will consider the application pf these methods.

8. Inductive Logic Programming - 1 Lecture

Inductive Logic Programming (ILP) is a new and exciting development in AI systems which has a wide range of applications. This lecture will introduce ILP and the issues associated with applying ILP to real world problems.

9. Data Mining - 1 Practical

Data mining has become one of the fastest growing application areas in AI. It combines various AI techniques such as neural networks and machine induction with statistical analysis and database techniques. A Data Mining tool will be considered during this practical.

G. Luger and W. Stubblefield (1989)

T.M. Mitchell (1998)

Janet L. Kolodner (1993)

This module is at CQFW Level 6