Software Engineering (faster route) BSc/MSci
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.
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.