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.
Linked modules
Pre-requisites: STAT6114 or STAT6103
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Choose, compare and use appropriate machine learning techniques to address specific prediction/classification problems;
- Contrast the statistical modelling and machine learning approaches for the analysis of data;
- 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.
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.
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.
| Type | Hours |
|---|---|
| Teaching | 30 |
| Independent Study | 70 |
| Total study time | 100 |
Resources & Reading list
Journal Articles
Breiman, L. (2001). Statistical modeling: The two cultures.. Statistical science, 16(3), pp. 199-231.
Textbooks
Friedman, J., Hastie, T. and Tibshirani, R. (2017). The elements of statistical learning.. Springer.
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An introduction to statistical learning with applications in R.. Springer.
Assessment
Assessment strategy
The course will be assessed by a written report representing 100% of the marks.
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Project report | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Project report | 100% |
Repeat Information
Repeat type: Internal & External