Introductory description

This module will introduce the foundational concepts in artificial intelligence. This module is only available to students in the second year of their degree and is not available as an unusual option or to students in other years of study.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1. Introduction
2. Rational Agents
3. Uninformed and Heuristic Search
4. Constraint Satisfaction Problems and Local Search
5. Adversarial Search
6. Knowledge Representation and Planning
7. Supervised Machine Learning
8. Bayesian AI
9. Unsupervised Machine Learning
10. Reinforcement Learning

Learning outcomes

By the end of the module, students should be able to:

• Develop an appreciation of AI through the concept of rational agency
• Understand various methods for search (uninformed, heuristic and adversarial) and constraint satisfaction problems
• Understand a variety of methods for knowledge representation along with basic reasoning and planning approaches
• Develop an appreciation of machine learning, including supervised and unsupervised methods
• Understand various methods for representing and reasoning under uncertainty

Indicative reading list

Reading lists can be found in Talis

Specific reading list for the module

Subject specific skills

• Develop an appreciation of AI through the concept of rational agency
• Understand various methods for search (uninformed, heuristic and adversarial) and constraint satisfaction problems
• Understand a variety of methods for knowledge representation along with basic reasoning and planning approaches
• Develop an appreciation of machine learning, including supervised and unsupervised methods
• Understand various methods for representing and reasoning under uncertainty.

Transferable skills

Problem solving
Critical thinking