Skip to main navigationSkip to main content
The University of Southampton

STAT6121 Machine Learning

Module Overview

The module aims to equip students with the necessary foundations to make practical and effective use of machine learning methods on complex datasets. This course uses R and is delivered as an intensive one-week module for the MSc in Data Analytics for Government.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • A broad set of machine learning techniques and their use in practice.
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Contrast the statistical modelling and machine learning approaches for the analysis of data;
  • Choose, compare and use appropriate machine learning techniques to address specific prediction/classification problems;
  • Assess the uncertainty associated to a given machine learning application using appropriate statistical measures;
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Communicate the results of machine learning applications to specialized and non-specialized audiences.


- Introduction to the statistical modelling and machine learning approaches. - Prediction for continuous responses: Linear regression, shrinkage - Classification: Linear Discriminant Analysis (LDA), logistic regression, classification trees, Support Vector Machines (SVM) - Dimensionality reduction: Principal Components Analysis (PCA) - Clustering algorithms - Ensemble methods: Bagging and Boosting - Case studies

Learning and Teaching

Teaching and learning methods

Depending on feasibility, teaching may be delivered face to face intensively over a week, or online using a mixture of synchronous and asynchronous online methods, which may include lectures, discussion boards, workshop activities, exercises, and videos. A range of resources will also be provided for further self-directed study.

Independent Study70
Total study time100

Resources & Reading list

James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An introduction to statistical learning with applications in R.. 

Friedman, J., Hastie, T. and Tibshirani, R. (2017). The elements of statistical learning. . 

Breiman, L. (2001). Statistical modeling: The two cultures. . Statistical science. ,16 , pp. 199-231.


Assessment Strategy

There will be opportunities to evaluate your progress through formative assessment, with summative assessment based on one online assignment.


MethodPercentage contribution
Project report  (4000 words) 100%


MethodPercentage contribution
Project report  (4000 words) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisites: STAT6114 or STAT6103


Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

Books and Stationery equipment

Recommended texts for this module may be available in limited supply in the University Library and students may wish to purchase reading texts as appropriate.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

Share this module Share this on Facebook Share this on Twitter Share this on Weibo
Privacy Settings