2024/25 Taught Postgraduate Module Catalogue
OMAT5200M Machine Learning
15 creditsClass Size: 100
Module manager: TBC
Email: .
Taught: 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr) View Timetable
Year running 2024/25
Pre-requisite qualifications
NonePre-requisites
| OMAT5100M | Programming for Data Science |
Module replaces
N/AThis module is not approved as an Elective
Module summary
Machine learning is a rapidly developing research area which takes an algorithmic approach to identifying patterns and statistical regularities in data without or with limited human intervention, often with the aim of supporting decision making. In this module you will learn to apply a number of machine learning techniques that are widely used in industry, government, and other large organisations. You will learn how the different approaches relate to and are motivated by statistics and will gain practical experience in the application of these techniques on real and simulated datasets.Objectives
The module aims to give students the skills and experience to produce simple computer-based applications for a range of sectors based on widely used machine learning techniques such as linear regression, neural networks and decision trees. It prepares students to develop and integrate systems using data analysis techniques.Learning outcomes
On completion of this module students should be able to:
1. State the main algorithms that are used in machine learning
2. Explain the ideas underlying these algorithms
3. Explain the relative strength and weaknesses of these algorithms
4. Implement these methods, or use existing implementations to explore data sets and build models
5. Evaluate the performance of the different algorithms
Skills outcomes
Skills developed in this module include:
- interpreting data and making decisions based on that interpretation.
- programming and use of statistical software.
Syllabus
Indicative content for this module includes:
Neural networks, decision trees, support vector machines, Bayesian learning, instance-based learning, linear regression, clustering, reinforcement learning, recent developments in machine learning. Examples will be drawn from simple problems that arise in data analytics and related areas.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| On-line Learning | 5 | 1.00 | 5.00 |
| Discussion forum | 6 | 2.00 | 12.00 |
| Seminar | 1 | 1.50 | 1.50 |
| Independent online learning hours | 42.00 | ||
| Private study hours | 89.50 | ||
| Total Contact hours | 18.50 | ||
| Total hours (100hr per 10 credits) | 150.00 | ||
Opportunities for Formative Feedback
Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow opportunities for formative feedback from peers and tutors.Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| In-course Assessment | In-course MCQ | 20.00 |
| In-course Assessment | In-course MCQ | 20.00 |
| Assignment | Lab exercise report | 60.00 |
| Total percentage (Assessment Coursework) | 100.00 | |
Students will resit by completing the Assignment in conjunction with a portion of the in-course assessment six months after the delivery of the module.
Reading list
There is no reading list for this moduleLast updated: 29/04/2024 16:18:46
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