Postgraduate

Modules

Modules

MAS8383 : Statistical Learning Methodology

Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0

Aims

More data than ever before are being generated and stored, in a variety of fields across industry. The term "big data" has emerged in acknowledgement of the vast amounts of data now available. By applying statistical analyses to these data sets, we can start to use them to answer important questions such as (i) which are the important factors affecting the quality of an industrial process; (ii) how many different types of customer are interested in your product. Commonly the data sets that arise in industry are multivariate, comprising a large number of observations on many variables. In this module we study how we can learn from data sets of this form. Attention is paid to the statistical and mathematical theory underpinning the methods, in addition to their practical application using R.

Specifically, the module aims to equip students with the following knowledge and skills:

- To gain an understanding of the theory behind modern statistical approaches to learning from data.
- To gain experience in the application of these techniques to the analysis of large and complex data sets across a range of application areas.

Outline Of Syllabus

- Fundamentals of multivariate data analysis, including data representation, summary and core applications of linear algebra
- Theory and practice of principal components analysis
- Cluster analysis
- Linear regression, including variable selection, regularisation (ridge regression, the lasso, the elastic net) and dimension-reduction techniques
- Classification, including discriminant analysis and logistic regression
- Generalized linear models
- Tree-based methods, including regression trees, classification trees and random forests

Teaching Methods

Module leaders are revising this content in light of the Covid 19 restrictions.
Revised and approved detail information will be available by 17 August.

Assessment Methods

Module leaders are revising this content in light of the Covid 19 restrictions.
Revised and approved detail information will be available by 17 August.

Reading Lists

Timetable