Learning outcomes

• describe a range of standard artificial intelligence techniques including search, knowledge representation, machine vision, expert systems and machine learning;
• appreciate the strengths and weaknesses of different artificial intelligence technologies for real world applications;
• describe some of the contributions of artificial intelligence to computing in general, and to industry and commerce, particularly in the area of expert systems.

Brief description

Aims

Content

2. Intelligent Agents - 2 Lectures
How can we build an intelligent robot? Sensing, Action and Cognition.
The symbolic approach and alternatives. Computers and Brains.

3. Machine Vision - 3 Lectures
How can robots see? The nature of the vision task.
Computer vision and image processing.

4. Search - How can computers find solutions? 5 lectures
Why do we need search? Evaluation of search strategies.
Un-informed search techniques. Informed search techniques.

5. Machine Learning - How can a computer program learn? 3 lectures
Game playing. Genetic algorithms. Decision tree learning.

6. Knowledge - 2 lectures
How can robots think? Knowledge representation methods.
Reasoning and inference.

7. Expert Systems - 2 Lectures
How can human expertise be automated? Example applications and commercial
successes.
How to build a rule-based expert system, - operation and worked examples.

8. Artificial Brains - 2 Lectures
How can robot brains be built? Artificial neural nets, pattern recognition
and learning.

Reading Lists