Data Mining and Machine Learning STATS5099

  • Academic Session: 2023-24
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Either Semester 1 or Semester 2
  • Available to Visiting Students: No

Short Description

This course introduces students to modern data mining and machine learning techniques, with an emphasis on practical issues and applications.


Mainly consists of asynchronous learning material and drop in tutorial help rooms

Requirements of Entry

Places on this course are limited. Entry is only guaranteed for those students on a programme for which this is a compulsory course

Excluded Courses

Data Mining and Machine Learning 1 (ODL)

Machine Learning

Machine Learning (Level M)


End-of-course examination (80%); coursework (20%)


Reassessment will, generally, not be available for the coursework.

Main Assessment In: April/May

Course Aims

The aims of this course are:

■ to introduce students to different methods for dimension reduction and clustering (unsupervised learning);

■ to introduce students to a range of classification methods;

■ to equip students to apply 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:

■ apply and interpret methods of dimension reduction such as principal component analysis;

■ apply and interpret classical methods for cluster analysis;

■ apply and interpret a wide range of methods for classification;

■ select appropriate machine learning methods to solve real-world problems of moderate complexity.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.