ST405 Half Unit
Unsupervised Machine Learning and Multivariate Data Analysis
This information is for the 2025/26 session.
Course Convenor
Dr Yunxiao Chen
Availability
This course is available on the MRes in Management (Marketing), MSc in Data Science, MSc in Health Data Science, MSc in Marketing, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available with permission as an outside option to students on other programmes where regulations permit. This course uses controlled access as part of the course selection process.
How to apply: Priority is given to students from the Departments of Statistics, including students on the MSc in Health Data Science and those with the course listed in their programme regulations.
Students should check that they meet the pre-requisites in the course guide before applying, but do not need to provide a written statement. Providing a statement will not aid a student’s chances of being accepted onto a course and statements are not read.
Deadline for application: Due to the nature of the method of application, interested students should apply as soon as possible after the opening selection and no later than 10.00am on Friday 26 September.
Course lecturers will aim to make initial offers to students on LSE For You by Friday 26 September.
For queries contact: Stats-Msc@lse.ac.uk
Requisites
Pre-requisites:
Students must have completed MA212 and ST202 before taking this course.
Course content
An introduction to unsupervised machine learning and multivariate data analysis: Matrix algebra, principal component analysis, cluster analysis, latent class model, factor models, rank aggregation, directed graphical models, and topic models.
Teaching
This course has a reading week in Week 6 of Winter Term.
This course will be delivered through a combination of computer workshops and lectures, totalling a minimum of 28 hours across Winter Term. This course includes a reading week in Week 6 of Winter Term.
Formative assessment
Coursework assigned fortnightly and returned to students via Moodle with comments/feedback before the computer workshops.
Indicative reading
- D J Bartholomew, F Steele, I Moustaki & J Galbraith, Analysis of Multivariate Social Science Data (2nd edition);
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning;
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning (2nd edition);
- Bartholomew, D. J., Knott, M., & Moustaki, I. (2011). Latent variable models and factor analysis: A unified approach (3rd edition).
Assessment
Exam (100%), duration: 120 Minutes in the Spring exam period
Key facts
Department: Statistics
Course Study Period: Winter Term
Unit value: Half unit
FHEQ Level: Level 7
CEFR Level: Null
Keywords: unsupervised learning, principal component analysis, clustering, factor models, rank aggregation, directed acyclic graphs, topic models
Total students 2024/25: 6
Average class size 2024/25: 6
Controlled access 2024/25: NoCourse selection videos
Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.
Personal development skills
- Problem solving
- Application of numeracy skills
- Specialist skills