2011/12 Undergraduate Module Catalogue

COMP2240 Artificial Intelligence

20 creditsClass Size: 70

Module manager: Dr Derek Magee
Email: D.R.Magee@leeds.ac.uk

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2011/12

Pre-requisites

COMP1640

Modelling, Analysis and Algorithm Design

This module is not approved as an Elective

Objectives

On completion of this module, students should be able to:
- understand the scope and nature of artificial intelligence in the discipline of computing;
- understand the fundamental ideas and techniques within the main approaches to artificial intelligence;
- implement a simple intelligent system by applying suitable theoretical concepts from artificial intelligence;
- appreciate how complex real world processes (e.g. biology, language, speech, reasoning, etc.) can be analysed and modelled, and of the role of these techniques in developing artificial intelligence applications;
- understand the main concepts of text mining and corpus processing;
- understand the main concepts in signal processing and their relevance to areas such as speech and image processing and bioinformatics;
- understand the main concepts in statistical learning and optimization;
- understand the main concepts of knowledge representation and inference.

Syllabus

- Overview of AI, its scope, history, and prospects. Search techniques and problem solving (informed and uninformed search, heuristic search, genetic algorithms, game playing). Uncertainty (applications of probability, Bayesian networks, Hidden Markov Models). Basic concepts of machine learning.
- Corpora; web-as-corpus text capture, cleansing, tokenisation; type-token frequency distributions, Zipf's law; stemming, morphology, collocations, concordances; Information Retrieval.
- Signal Acquisition, Linear transforms, Filtering, Temporal normalisation/alignment, Spatial normalisation, Selected applications.
- Topics in statistical learning.
- Knowledge Representation and Reasoning; First order predicate calculus; inference (including resolution); representing knowledge in logic.

Teaching methods

Delivery type	Number	Length hours	Student hours
Class tests, exams and assessment	1	2.00	2.00
Class tests, exams and assessment	1	3.00	3.00
Lecture	44	1.00	44.00
Practical	20	1.00	20.00
Private study hours			131.00
Total Contact hours			69.00
Total hours (100hr per 10 credits)			200.00

Private study

- Taught session prep: 36 hours
- Taught session follow-up: 36 hours
- Self-directed study: 14 hours
- Assessment activitires: 45 hours.

Opportunities for Formative Feedback

Attendance and formative assessment.

Methods of assessment

Exams

Exam type	Exam duration	% of formal assessment
Open Book exam	3 hr 00 mins	100.00
Open Book exam	2 hr 00 mins	0.00
Total percentage (Assessment Exams)		100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 04/10/2011

Disclaimer

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