2023/24 Taught Postgraduate Module Catalogue

COMP5611M Machine Learning

15 creditsClass Size: 300

Module manager: Dr Yanlong Huang
Email: Y.L.Huang@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2023/24

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	40.00
Total percentage (Assessment Coursework)		40.00

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	60.00
Total percentage (Assessment Exams)		60.00

This module will be reassessed by open book examination.

Reading list

The reading list is available from the Library website

Last updated: 08/04/2024

Disclaimer

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