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The University of Southampton

COMP2208 Intelligent Systems

Module Overview

This module aims to give a broad introduction to the rapidly-developing field of artificial intelligence, and to cover the mathematical techniques used by this module and by other artificial intelligence modules in the computer science programme

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

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

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

Having successfully completed this module you will be able to:

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


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

Learning and Teaching

Follow-up work18
Completion of assessment task15
Preparation for scheduled sessions18
Wider reading or practice43
Total study time150

Resources & Reading list

Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. 

Copeland J, (1993). Artificial Intelligence: A Philosophical Introduction. 

Nilsson NJ (1998). Artificial Intelligence: A New Synthesis. 

Russell, S and Norvig, P (2003). Artificial Intelligence: A Modern Approach. 

Mitchell, T. (1997). Machine Learning. 



MethodPercentage contribution
Continuous Assessment 20%
Final Assessment  80%


MethodPercentage contribution
Set Task 100%


MethodPercentage contribution
Set Task 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisite: COMP1202

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