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2010/1 Module Catalogue
Module Provider: Computing Short Name: CS289
Level: HE2 Module Co-ordinator: TANG HL Dr (Computing)
Number of credits: 10 Number of ECTS credits: 5
Module Availability

Autumn semester

Assessment Pattern

Assessment Pattern


Unit(s) of Assessment


Weighting Towards Module Mark ( %)


Coursework (individual)






Two Hour Unseen Examination




Qualifying Condition(s) 


A weighted aggregate mark of 40% is required to pass the module.





Module Overview

Module Overview


Computers have become commonplace in many areas of our lives and are able to accomplish many things that humans would find difficult, if not impossible, to do by their own unaided efforts. Whilst computers can perform many calculations in a very short time they generally do not possess the ability to learn or to reason about novel situations or to process incomplete or uncertain data. They will need knowledge of the environment in which they operate so that they can understand what their sensors are monitoring and so that they can behave rationally. This module demonstrates the basic principles and methods of Artificial Intelligence (AI) and provides the basis for understanding and later choosing the correct tools for building such systems. Applications that motivate the development of Artificial Intelligence technology include intelligent robots, automated navigation for autonomous vehicles, object recognition and tracking, medical diagnosis, language communications and many others. Any application that requires human-like intelligence is an application for Artificial Intelligence.





Module Aims

This module aims to demonstrate a variety of techniques for capturing human knowledge and represent it in a computer in a way that enables the machine to reason over the data represented and mimic the human ability to deal with incomplete or uncertain data. This module introduces the range of artificial intelligence elements that future robots or intelligent machines must possess as embedded implementations if they are to behave intelligently.


Learning Outcomes

Learning Outcomes


At the end of the module students should be able to:


  • Describe methods for acquiring and representing human knowledge.


  • Describe techniques for representing acquired knowledge in a way that facilitates automated reasoning over the knowledge.


  • Describe how AI systems are developed and work.


  • Demonstrate the ability to design and implement basic AI techniques.


  • Explain essential elements in various machine learning techniques.


  • Evaluate the use of machine learning techniques, highlighting their strengths and weaknesses.


  • Evaluate emerging AI techniques.


Module Content

Module Content


  • Knowledge Representation and Uncertainty Reasoning


o      Bayesian Network


o      Dempster-Shafer Theory


  • Learning


o      Introduction to Learning


o      Nearest Mean


o      kNN


o      Clustering


o      Decision Tree


o      Neural Networks


o      Support-Vector Machines


o      Genetic Algorithms


  • Visual Perception


o      Feature Extraction


o      Region Detection and Segmentation


o      Classification and Pattern Recognition


  • Natural Language Understanding


o      Syntax, Semantics and Context Analysis


o      Probabilistic Language Processing


o      Speech Processing and Hidden Markov Model



Methods of Teaching/Learning

Methods of Teaching/Learning


There are 10 teaching weeks in the Autumn Semester. Each week there will be


  • 2 hour lectures


  • 2 hour lab sessions


There will be a revision session after the teaching weeks.



Selected Texts/Journals

Selected Texts/Journals




  • Russell, S. J. and Norvig, P., (2010), Artificial Intelligence, A Modern Approach, third edition, Prentice Hall, Pearson Education International, ISBN-13: 978-0-13-207148-2.


  • Callan, R., (2003), Artificial Intelligence, Palgrave MacMillan, ISBN: 0333801369.


  • Efford, N., (2000), Digital Image Processing, A Practical Introduction using Java, Addison Wesley, ISBN 0201596237.


  • Negnevitsky, M., (2004), Artificial Intelligence: A Guide to Intelligent Systems (2nd Edition), Addison Wesley, ISBN: 0321204662.




  • Winston, P.H, (1992), Artificial Intelligence (3rd Edition), Addison Wesley, ISBN: 0201533774.



Last Updated

AUG 2010 JG