2022/23 Taught Postgraduate Module Catalogue
COMP5611M Machine Learning
15 creditsClass Size: 300
Module manager: Dr Nishant Ravikumar
Email: n.ravikumar@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2022/23
This module is not approved as an Elective
Objectives
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.
Syllabus
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.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| Lecture | 22 | 1.00 | 22.00 |
| Practical | 10 | 2.00 | 20.00 |
| Private study hours | 108.00 | ||
| Total Contact hours | 42.00 | ||
| Total hours (100hr per 10 credits) | 150.00 | ||
Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Practical | Programming Project | 20.00 |
| In-course Assessment | Online test (Gradescope) | 30.00 |
| In-course Assessment | Online test (Gradescope) | 50.00 |
| Total percentage (Assessment Coursework) | 100.00 | |
Resits will be reassessed by online time-limited assessment
Reading list
The reading list is available from the Library websiteLast updated: 01/06/2022 16:59:02
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