Data Mining and Machine Learning STATS5099

  • Academic Session: 2024-25
  • 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
  • Collaborative Online International Learning: No

Short Description

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

Timetable

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)

Assessment

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

Main Assessment In: April/May

Are reassessment opportunities available for all summative assessments? No

Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

 

Reassessment will, generally, not be available for the coursework due to the nature of group work. 

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.