Data Mining and Machine Learning I: Supervised and Unsupervised Learning (ODL) STATS5074

  • Academic Session: 2019-20
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Summer
  • Available to Visiting Students: No
  • Available to Erasmus Students: No
  • Taught Wholly by Distance Learning: Yes

Short Description

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

Timetable

The course mostly consists of asynchronous teaching material.

Requirements of Entry

The course is only available to online-distance learning students on the PGCert/PGDip/MSc in Data Analytics and Data Analytics for Government.

Excluded Courses

Machine Learning

Machine Learning (Level M)

Co-requisites

-/-

Assessment

100% Continuous Assessment

This will typically be made up of a project (40%), two oral assessments (40%) and one homework exercise / online quiz (20%). Full details are provided in the programme handbook.

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

■ fit 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.