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Statistical Learning


  • Credit value: 15 credits at Level 7
  • Convenor: Dr Brad Baxter
  • Assessment: to be confirmed

Module description

In this module we introduce you to the techniques of statistical learning. We provide you with a unified treatment and understanding of the mathematical and statistical basis of a variety of methods for classification, regression and cluster analysis. You will also be given computational experience in applying the relevant methods using a high-level programming language such as R.

Indicative 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

Learning objectives

By the end of this module, you should be able to:

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