School of Computer Science

Module 06-20236 (2011)

Machine Learning (Extended)

Level 4/M

Ata Kaban Semester 1 10 credits
Co-ordinator: Ata Kaban
Reviewer: Xin Yao

The Module Description is a strict subset of this Syllabus Page.


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:

  • Demonstrate a knowledge and understanding of the main approaches to machine learning
  • Demonstrate the ability to apply the main approaches to unseen examples
  • Demonstrate an understanding of the differences, advantages and problems of the main approaches in machine learning
  • Demonstrate an understanding of the relationship between machine learning and human learning
  • Demonstrate an understanding of the main limitations of current approaches to machine learning, and be able to discuss possible extensions to overcome these limitations
  • Demonstrate a practical understanding of the use of machine learning algorithms
  • survey and discuss the research literature in one subfield of machine learning

Taught with

Teaching methods

2 hrs lectures, exercise and laboratory classes per week.


  • Sessional: 1.5 hr examination (60%), continuous assessment (40%).
  • Supplementary: By examination only.

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

Programmes containing this module