Foundations of Artificial Intelligence

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Classical and modern approaches to AI
• The principal achievements and shortcomings of AI
• The philosophical basis of AI
• Challenges for the future of AI

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Identify the main techniques that have been used in AI, and their range of applicability

Introduction to AI

• Flavours of AI: strong and weak, neat and scruffy, symbolic and sub-symbolic, knowledge-based and data-driven.
• The computational metaphor. What is computation? Church-Turing thesis. The Turing test. Searle's Chinese room argument.

Search

• Finding satisfactory paths: depth-first and breadth-first, iterative deepening, local search and heuristic search. Finding optimal paths: branch and bound, dynamic programming, A*.

Representing Knowledge

• Production rules, monotonic and non-monotonic logics, semantic nets, frames and scripts, description logics.

Reasoning and Control

• Data-driven and goal-driven reasoning.

Reasoning under Uncertainty

• Probabilities, conditional independence, causality, Bayesian networks, belief propagation.

Machine Learning

• Inductive and deductive learning, unsupervised and supervised learning, reinforcement learning, concept learning from examples, Quinlan's ID3, classification and regression trees, Bayesian methods.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.