Module 02640 (2011)

Syllabus page 2011/2012

Machine Learning

Level 2/I

Ata Kaban
10 credits in Semester 1

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The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)

Relevant Links

Module web page


The module will provide a good foundation to machine learning. It will compare and contrast human learning with machine learning. It will examine the limitations of machine learning, the role of hypothesis bias and hypothesis representation.


The aims of this module are to:

  • introduce the basic concepts and terminology of machine learning
  • give an overview of the main approaches to machine learning (with the exception of neural nets, which are dealt with in a separate module)
  • show similarities and differences between different approaches
  • present basic principles for the classification of approaches to machine learning
  • give practical experience of applying machine learning algorithms to classification and data analysis problems

Learning Outcomes

On successful completion of this module, the student should be able to: Assessed by:
1demonstrate a knowledge and understanding of the main approaches to machine learning Examination
2demonstrate the ability to apply the main approaches to unseen examples Examination, Continuous Assessment
3demonstrate an understanding of the differences, advantages and problems of the main approaches in machine learning Examination, Continuous Assessment
4demonstrate an understanding of the relationship between machine learning and human learning Examination
5demonstrate an understanding of the main limitations of current approaches to machine learning, and be able to discuss possible extensions to overcome these limitations Examination, Continuous Assessment
6demonstrate a practical understanding of the use of machine learning algorithms Continuous Assessment

Restrictions, Prerequisites and Corequisites


May not be taken by anyone who has taken or is taking 06-20236 (Machine Learning (Extended)).


06-23069 (Introduction to AI) (or equivalent)




Teaching Methods:

2 hrs lectures, exercise and laboratory classes per week.

Contact Hours:



  • Sessional: 1.5 hr examination (80%), continuous assessment (20%).
  • Supplementary (where allowed): By examination only.

Recommended Books

TitleAuthor(s)Publisher, Date
Machine LearningT MitchellMcGraw Hill, 1997
Reinforcement Learning: an IntroductionR Sutton & A. BartoMIT Press, 1998
Support Vector Machines and Other Kernel-Based Learning MethodsNello Cristianini & John Shawe-Taylor2000
Pattern ClassificationR.O. Duda, P.E. Hart & D.G.Stork2000
Modelling the Internet and the Web Pierre Baldi, Paolo Frasconi, and Padhraic Smyth 2003
Artificial Intelligence, a Modern ApproachRussell S & Norvig P1995

Detailed Syllabus

  1. Basic notions of learning, introduction to learning algorithms, literature
  2. Probabilistic Sequence models (Random sequence model, 1st order Markov model)
  3. Reinforcement Learning in deterministic environments
  4. Reinforcement Learning in non-deterministic environments
  5. Unsupervised Learning; Dimensionality reduction; Latent Semantic Analysis
  6. Independent Component Analysis. The cocktail party problem.
  7. Introduction to Bayesian learning; Naive-Bayes classification
  8. Decision Tree Learning
  9. Instance-Based Learning (k-nearest neighbour, Case-based Reasoning)
  10. Support Vector and Kernel Machines
  11. Clustering and visual data analysis
  12. Revision

Last updated: 2 Mar 2006

Source file: /internal/modules/COMSCI/2011/xml/02640.xml

Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus