Module manager: Dr Yanlong Huang
Email: Y.L.Huang@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
This module is not approved as a discovery module
On completion of this module, students should be able to:
• list the principal algorithms used in machine learning, and derive their update rules
• appreciate the capabilities and limitations of current approaches;
• evaluate the performance of machine learning algorithms;
• use existing implementation(s) of machine learning algorithms to explore data sets and build models.
Topics selected from:
Neural networks, decision trees, support vector machines, Bayesian learning, instance-based learning, linear regression, clustering, reinforcement learning, deep learning.
Methods for evaluating performance.
Examples will be drawn from simple problems that arise in studies of robotics and computer vision.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 22 | 1 | 22 |
| Practical | 10 | 2 | 20 |
| Private study hours | 108 | ||
| Total Contact hours | 42 | ||
| Total hours (100hr per 10 credits) | 150 | ||
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Practical | Programming Project | 40 |
| Total percentage (Assessment Coursework) | 40 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Open Book exam | 2.0 Hrs 0 Mins | 60 |
| Total percentage (Assessment Exams) | 60 | |
This module will be reassessed by open book examination.
The reading list is available from the Library website
Last updated: 9/25/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team