2024/25 Undergraduate Module Catalogue
COMP3611 Machine Learning
10 creditsClass Size: 320
Module manager: Dr Marc de Kamps
Email: M.deKamps@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2024/25
Pre-requisites
| COMP2611 | Artificial Intelligence |
This module is not approved as a discovery module
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 |
| Tutorial | 11 | 1.00 | 11.00 |
| Private study hours | 47.00 | ||
| Total Contact hours | 53.00 | ||
| Total hours (100hr per 10 credits) | 100.00 | ||
Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| In-course Assessment | Coursework | 30.00 |
| Total percentage (Assessment Coursework) | 30.00 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Exams
| Exam type | Exam duration | % of formal assessment |
| Open Book exam | 2 hr 00 mins | 70.00 |
| Total percentage (Assessment Exams) | 70.00 | |
This module will be reassessed by open book examination.
Reading list
The reading list is available from the Library websiteLast updated: 25/09/2024 09:18:38
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