School of Computer Science

Module 02640 (2011)

Module description - Machine Learning

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

Module Title Machine Learning
School School of Computer Science
Module Code 06-02640
Level 2/I
Member of Staff Ata Kaban
Semester Semester 1 - 10 credits

2 hrs lectures, exercise and laboratory classes per week.


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
  • Sessional: 1.5 hr examination (80%), continuous assessment (20%).
  • Supplementary: By examination only.
Artificial Intelligence, a Modern Approach Russell S & Norvig P
Machine Learning T Mitchell McGraw Hill
Modelling the Internet and the Web Pierre Baldi, Paolo Frasconi, and Padhraic Smyth
Pattern Classification R.O. Duda, P.E. Hart & D.G.Stork
Reinforcement Learning: an Introduction R Sutton & A. Barto MIT Press
Support Vector Machines and Other Kernel-Based Learning Methods Nello Cristianini & John Shawe-Taylor