Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Data Mining and Machine Learning STATS5099

  • Academic Session: 2021-22
  • 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
  • Available to Erasmus Students: No

Short Description

This course introduces students to machine learning methods and modern data mining 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)


100% final exam

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 introduce students to neural networks and deep learning;

■ to introduce students to kernel methods and support vector machines.

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, multidimensional scaling and the biplot;

■ apply and interpret classical methods for cluster analysis;

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

■ explain and interpret ROC curves and performance measures such as AOC

■ assess the fit of neural networks and support vector machines to data and assess their predictive ability objectively.

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.