Intelligent Systems

Learning Outcomes

Knowledge and Understanding

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

• The difficulty of distinguishing AI from advanced computer science in general
• The principal achievements and shortcomings of AI
• The main techniques that have been used in AI, and their range of applicability
• Basic differential and integral calculus
• Likely future developments in AI

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Assess the applicability of AI techniques in novel domains
• Assess the validity of approaches to model intelligent processing
• Select appropriately from a range of techniques for intelligent systems
• Assess the claims of AI practitioners as they relate to `intelligence'

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. Calculus - Differentiation - standard rules; Newton's method for finding roots; partial differentiation; integration - standard integrals; integration by parts; numerical integration. Search - Finding satisfactory paths and outcomes; chosen from: depth-first and breadth-first, iterative deepening, evolutionary algorithms, hill-climbing and gradient descent, beam search and best-first. 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, AND/OR graphs, truth-maintenance systems, abduction and uncertainty. Reasoning under Uncertainty - Probabilities, conditional independence, causality, Bayesian networks, noisy-OR, d-separation, 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. Key Application Areas, selected from: - Expert system, decision support systems - Speech and vision - Natural language processing - Information Retrieval - Semantic Web

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.