Statistical Learning

• Credit value: 15 credits at Level 7
• Tutor: Georgios Papageorgiou

This module introduces you to the techniques of statistical learning. It provides a unified treatment and understanding of the mathematical and statistical basis of a variety of methods for classification, regression and cluster analysis. It also gives you computational experience in applying the relevant methods using a high-level programming language such as R.

indicative module Syllabus

Supervised learning:

• Linear regression and locally weighted linear regression
• Generalised linear models for binary and multi-class classification
• Discriminant analysis
• Support vector machines
• Neural networks
• Learning theory, assessing performance and cross validation
• Classification and regression trees

Unsupervised learning:

• Introduction to cluster analysis
• Hierarchical clustering methods

By the end of this module, you should:

• have knowledge and understanding of the methodology, the main techniques and the underpinning mathematical and statistical theory in statistical learning
• be able to make sensible use of a range of suitable techniques, models and algorithms to elicit useful relations or structure within datasets
• be able to use advanced mathematical and statistical software to explore datasets
• be able to incorporate the results of a technical analysis into clearly written report form that may be understood by a non-specialist.