Module 06-27110 (2016)

This module provides a general introduction to artificial intelligence, its techniques, and main subfields. The principal focus of the module will be on the common underlying principles, such as knowledge representation, search, and learning.

• Provide a general introduction to artificial intelligence, its techniques and its main subfields.
• Give an overview of key underlying ideas, such as knowledge representation, reasoning, search, and learning.
• Demonstrate the need for different approaches for different problems.

1. Discuss the major issues and techniques in a variety of sub-fields of AI, such as vision, robotics, natural language processing, planning, probabilistic reasoning, and machine learning.
2. Compare common AI techniques, describing their strengths and weaknesses.
3. Apply a variety of standard AI techniques to simple examples.

None

Large Group Lectures and Lab sessions in Computer Science Labs.

Contact Hours:

39

Sessional: Assessments: 1.5 hr Examination (70%) Continuous Assessment (30%)

Supplementary (where allowed): 1.5 hr Examination (100%)

1. Overview of AI terms and concepts
2. Rational Agents
3. Search Algorithms
4. Optimality and Heuristics in Search
5. Game Algorithms
6. Probability Theory
7. Monte Carlo Methods
8. Belief Networks and Bayesian Inference
9. Markov Chains and Decision Processess
10. Reinforcement Learning