Machine Learning (H) COMPSCI4061
- Academic Session: 2019-20
- School: School of Computing Science
- Credits: 10
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
- Available to Erasmus Students: Yes
A practical introduction to the foundations of machine learning.
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.
Machine Learning (M), Machine Learning for Data Scientists (M)
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. 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.
The coursework cannot be redone because the feedback provided to the students after the original coursework would give any students redoing the coursework an unfair advantage.
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. Discuss the principle of learning from data;
2. Describe the main machine learning methods: regression, classification, clustering, probability density estimation and dimensionality reduction;
3. Use a selection of common machine learning algorithms and be aware of when one is to be favoured over other;
4. Implement and use machine learning algorithms in Matlab;
5. Discuss the major machine learning application area in, for example, Information Retrieval,
6. Human Computer Interaction, Bioinformatics and Computer Visions & Graphics;
7. Detail emerging machine learning approaches such as non-parametric methods and sampling techniques;
8. Produce a Multimedia System.
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.