Course Catalogue

Multivariate Statistics and Machine Learning 2 STATS4077

  • Academic Session: 2026-27
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

This course introduces students to advanced multivariate statistics and machine learning techniques, with a focus on developing a solid understanding of the methods and their practical applications.

Timetable

20 x 1 hour lectures

5 x 1 hour tutorials

5 x 1 hour labs

Requirements of Entry

Multivariate Statistics and Machine Learning 1

Excluded Courses

Multivariate Statistics and Machine Learning 2 (Level M)

Assessment

90-minute end-of-course examination (75%); lab report (25%).

Main Assessment In: April/May

Course Aims

• to introduce students to a range of classical and contemporary classification, clustering and dimension reduction methods;

• to equip students to apply and implement machine learning methods to solve applied problems;

• to train students to communicate the results of their analyses in clear non-technical language.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

• explain and critically compare a wide range of methods for classification, including support vector machines, tree-based methods and ensemble learning;

• explain and critically compare a range of advanced methods for clustering and dimension reduction;

• explain methods for structural inference in graphical models;

• demonstrate knowledge about the limitations of machine learning methods and ways of assessing and improving their performance;

• select, implement and evaluate machine learning methods on real-world problems of moderate complexity.

Minimum Requirement for Award of Credits

No exceptions