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 an Elective
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 | Delivery type 22 | Number 1 | Length hours 22 |
| Practical | Delivery type 10 | Number 2 | Length hours 20 |
| Private study hours | Delivery type 108 | ||
| Total Contact hours | Delivery type 42 | ||
| Total hours (100hr per 10 credits) | Delivery type 150 | ||
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Assessment type Practical | Notes Programming Project | % of formal assessment 40 |
| Total percentage (Assessment Coursework) | Assessment type 40 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Exam type Open Book exam | Exam duration 2.0 Hrs 0 Mins | % of formal assessment 60 |
| Total percentage (Assessment Exams) | Exam type 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