Course Catalogue

Machine Learning (H) COMPSCI4061

  • Academic Session: 2026-27
  • School: School of Computing Science
  • Credits: 10
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 1
  • Available to Visiting Students: Yes
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

A practical introduction to the foundations of machine learning.

Timetable

3 hours per week.

Requirements of Entry

Mandatory Entry Requirements Working knowledge of mathematics (e.g., matrices, linear spaces and basic geometry, as covered in, for example, Math1RS or Math1RT).

 

Recommended Entry Requirements Some experience in probability and statistics would be useful but is not essential.

Excluded Courses

Machine Learning (M), Machine Learning for Data Scientists (M)

Co-requisites

None

Assessment

Examination 50%, Coursework 50%.

Main Assessment In: April/May

Course Aims

To present students with an introduction to the general theory of learning from data and to a number of popular Machine Learning methods.

Intended Learning Outcomes of Course

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

1. Demonstrate knowledge of the major machine learning application areas in, for example Information Retrieval, Human Computer Interaction, Bioinformatics and Computer Vision & Graphics;

2. Explain the principle of learning from data;

3. Implement and use machine learning algorithms in Python;

4. Apply the main machine learning methods: regression, classification, clustering, probability density estimation and dimensionality reduction;

5. Explain the typical strengths and weaknesses of a selection of common algorithms

6. Appreciate some advanced machine learning approaches, e.g. non-parametric methods, sampling techniques and neural networks.

Minimum Requirement for Award of Credits

No exceptions