School of Computer Science

Module 26428 (2019)

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 06-26428
Level 3/H
Member of Staff Iain Styles
Semester Semester 1 - 10 credits

2 hrs lectures, exercise and laboratory classes per week.

Contact Hours:



The module will provide a solid foundation to machine learning. It will give an overview of many of the core concepts, methods, and algorithms in machine learning, covering several forms of supervised and unsupervised learning. It also introduces the basics of computational learning theory, leading up to more advanced topics like boosting and ensemble methods. The module will give the student a good understanding of how, why and when do various modern machine learning methods work. It will also give them experience of applying machine learning methods in practice, and an awareness of the issues, techniques and open problems posed by high dimensionality in machine learning.


On successful completion of this module, the student should be able to:

  1. demonstrate a knowledge and understanding of the main approaches to machine learning
  2. demonstrate the ability to apply the main approaches to unseen examples
  3. demonstrate an understanding of the differences, advantages and problems of the main approaches in machine learning
  4. demonstrate an understanding of the main limitations of current approaches to machine learning, and be able to discuss possible extensions to overcome these limitations
  5. demonstrate a practical understanding of the use of machine learning algorithms

Sessional: 1.5 hr examination (80%), continuous assessment (20%).

Supplementary (where allowed): By examination only.