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The University of Southampton
Courses

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.

Syllabus

- 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

The course will include lectures and practical sessions in R, mixed in a 5 day course designed for students on release from the workplace. Students are also expected to read wider than the lecture material as part of their individual study, and to critically appraise different approaches.

TypeHours
Teaching30
Independent Study70
Total study time100

Resources & Reading list

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

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

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

Assessment

Assessment Strategy

The course will be assessed by a written report representing 100% of the marks.

Summative

MethodPercentage contribution
Project report  (4000 words) 100%

Referral

MethodPercentage contribution
Project report  (4000 words) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisites: STAT6114 or STAT6103

Costs

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 www.calendar.soton.ac.uk.

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