Module manager: Dr Wesley Doorsamy
Email: w.doorsamy@leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2025/26
This module is not approved as a discovery module
This module provides students with the fundamentals of machine learning. It introduces basic machine learning concepts and algorithms to students serving as a foundation to more specialised topics in artificial intelligence. The module also serves as a hands-on introduction to the application of machine learning for tackling problems using pattern recognition, data-driven methods and statistical techniques.
This module has the following objectives:
- To study core concepts, algorithms and techniques in machine learning
- To learn how to apply algorithms and techniques in machine learning using Python.
- To provide a hands-on approach to the study of machine learning to develop students' practical skills with the tools and techniques for problem solving
- To establish a foundational knowledge on which to further develop more specialised knowledge in artificial intelligence.
On successful completion of the module students will have demonstrated the following learning outcomes:
1. Apply knowledge of mathematics, statistics, natural science and engineering principles to the solution of broadly-defined machine learning problems.
2. Analyse broadly-defined machine learning problems to reach substantiated conclusions first principles of mathematics, statistics, natural science and engineering principles.
3. Apply appropriate computational and analytical techniques to model broadly-defined machine learning problems.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills:
a) Application of science, mathematics and/or engineering principles
b) Problem analysis
c) Application of computational and analytical techniques
- Introduction to basic concepts in machine learning
- Probability, statistics and mathematics for machine learning
- Introduction to different methods, techniques and algorithms in pattern recognition and machine Learning
- Hands-On Machine Learning using modern toolkits, libraries, and frameworks
- Processing and post-processing, testing and evaluation
- Case-studies applying the learnt knowledge
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Consultation | 4 | 1 | 4 |
| Practical | 8 | 2 | 16 |
| Seminar | 32 | 1 | 32 |
| Independent online learning hours | 36 | ||
| Private study hours | 112 | ||
| Total Contact hours | 52 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Students studying ELEC modules will receive formative feedback in a variety of ways, including the use of self-test quizzes on Minerva, practice questions/worked examples and (where appropriate) through verbal interaction with teaching staff and/or post-graduate demonstrators.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| In-course Assessment | Coursework 1 | 25 |
| In-course Assessment | Coursework 2 | 50 |
| In-course Assessment | In-class Test | 25 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 30/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team