Machine Learning

Short Description

A practical introduction to the foundations of machine learning.

Timetable

3 hours per week.

Requirements of Entry

Mandatory Entry Requirements Working knowledge of mathematics (e.g., matrices, linear spaces and basic geometry, as covered in, for example, Math1RS or Math1RT).

Recommended Entry Requirements Some experience in probability and statistics would be useful but is not essential.

Assessment

Practical coursework 20% and examination 80%.

The coursework cannot be redone because the feedback provided to the students after the original coursework would give any students redoing the coursework an unfair advantage.
Main Assessment In: April/May

Course Aims

To present students with an introduction to the general theory of learning from data and to a number of popular Machine Learning methods.

Intended Learning Outcomes of Course

By the end of the course students will be able to:

1:Discuss the principle of learning from data;
2:Describe the main machine learning methods: regression, classification, clustering, probability density estimation and dimensionality reduction;
3:Use a selection of common machine learning algorithms and be aware of when one is to be favoured over other;
4:Implement and use machine learning algorithms in Matlab;
5:Discuss the major machine learning application area in, for example, Information Retrieval,
Human Computer Interaction, Bioinformatics and Computer Visions & Graphics;
6:Detail emerging machine learning approaches such as non-parametric methods and sampling techniques.