The University of Southampton
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COMP6231 Foundations of Artificial Intelligence

Module Overview

This course introduces the fundamental concepts of artificial intelligence (AI) and contains a coursework assignment to give you hands-on experience with the techniques. This unit aims to give a broad introduction to the rapidly-developing field of AI covering a range of approaches (modern, classical, symbolic, and statistical). This should prepare students for specialist options in semester 2. - Classical and modern approaches to AI - The principal achievements and shortcomings of AI. - The main techniques that have been used in AI, and their range of applicability - The philosophical basis of AI - Challenges for the future of AI

Aims and Objectives

Module Aims

To introduce the fundamental concepts 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:

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

Syllabus

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.

Learning and Teaching

TypeHours
Follow-up work18
Lecture36
Preparation for scheduled sessions18
Revision10
Wider reading or practice42.5
Completion of assessment task25.5
Total study time150

Resources & Reading list

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

Assessment

Summative

MethodPercentage contribution
Coursework 35%
Exam  (1.5 hours) 50%
Group presentation 15%

Referral

MethodPercentage contribution
Exam  (3 hours) 100%

Repeat Information

Repeat type: Internal & External

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