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2021/22 Taught Postgraduate Module Catalogue

COMP5611M Machine Learning

15 creditsClass Size: 300

Module manager: Dr Ali Gooya

Taught: 1 Sep to 31 Jan (adv yr), Semester 1 (Sep to Jan), Semester 2 (Jan to Jun) View Timetable

Year running 2021/22

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.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours97.00
Total Contact hours42.00
Total hours (100hr per 10 credits)139.00

Methods of assessment

Assessment typeNotes% of formal assessment
PracticalProgramming Project20.00
In-course AssessmentOnline test (Gradescope)30.00
In-course AssessmentOnline test (Gradescope)50.00
Total percentage (Assessment Coursework)100.00

The coursework is on the application of a supervised learning algorithm to an existing, realistic, dataset. Students are asked to design an appropriate model, evaluate its performance, and analyse the effect of the parameters on the results. The coursework is implemented in python, and makes use of state-of-the-art machine learning libraries.

Reading list

The reading list is available from the Library website

Last updated: 15/03/2022 16:12:19


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