MS4H03 - Machine Learning 04 Dec 2020 - 31 Aug 2026 | Version 1
Associated Module Information
| Module Code: | MS4H03 | ||
|---|---|---|---|
| Module Title: | Machine Learning | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematics | ||
| Faculty Sub Group: | Maths | ||
| Module Leader: | Rebecca Peters | ||
| Module Team: | Stephanie Perkins | ||
| First Intended Intake: | JAN 2025 | Final Year of Intake: | 2025 |
| Date Closed: | |||
| Credit Value: | 10 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100359 - artificial intelligence | 100992 - machine learning | 101034 - statistical modelling |
| HECOS Code Weighting: | 25 | 50 | 25 |
Document Version Information
| Version | 1 |
|---|---|
| Valid From | 04 Dec 2020 |
| Valid To | 31 Aug 2026 |
Module Aims
To provide an understanding of the key concepts and techniques associated with machine learning in the context of Data/AI.
To learn how to choose appropriate methods to analyse data, in order to extract information and classify features to inform decision making processes directed at business goals.
Content Summary
This module focuses on combining graduates’ acquired analytical and computing skills and applying them in the desired field of Machine Learning. Topics include, data pre-processing, supervised and unsupervised learning paradigms, regression and classification algorithms.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 8 |
| Seminar | 8 |
| Tutorial | 8 |
| Independent Study | 40 |
| Directed Study | 36 |
| Total Hours Selected | 100 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | To understand the concepts of machine learning for Data/AI and compare and test a range of classification techniques. |
| LO2 | To classify features of data sources, analysing and interpreting the outputs of machine learning techniques in the context of practical situations in the area of Data/AI. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Set Exercise - Not Time Constrained (CW) | Set Tasks - not-time constrained 1 | Within a software package, select and apply a range of algorithms | 0 | 3000 | 70 | No | 40 |
| Portfolio | Portfolio 1 | Practical demonstration of code | 0 | N/A | 30 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Set Tasks - not-time constrained 1 | ✔ | ✔ | |
| Portfolio 1 | ✔ | ✔ | |