2023/24 Taught Postgraduate Module Catalogue
COMP5611M Machine Learning
15 creditsClass Size: 300
Module manager: Dr Nishant Ravikumar
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2023/24
This module is not approved as an Elective
ObjectivesOn 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|
|Private study hours||108.00|
|Total Contact hours||42.00|
|Total hours (100hr per 10 credits)||150.00|
Methods of assessment
|Assessment type||Notes||% of formal assessment|
|Total percentage (Assessment Coursework)||40.00|
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
|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 listThe reading list is available from the Library website
Last updated: 09/06/2023
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