COMP47460 Machine Learning (Blended Delivery)

Academic Year 2023/2024

The objective of this module is to familiarise students with the fundamental theoretical concepts in machine learning, as well as instructing students in the practical aspects of applying machine learning algorithms. Key techniques in supervised machine learning will be covered, such as classification using decision trees and nearest neighbour algorithms, and regression analysis. A particular emphasis will be placed on the evaluation of the performance of these algorithms. In unsupervised machine learning, a number of popular clustering algorithms will be presented in detail. Further topics covered include ensemble learning, dimension reduction, and recommender systems. COMP47490 requires strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module, students will be able to: 1) Distinguish between the different categories of machine learning algorithms; 2) Identify a suitable machine learning algorithm for a given application or task; 3) Run and evaluate the performance of a range of algorithms on real datasets using a standard machine learning toolkit.

Student Effort Hours: 
Student Effort Type Hours






Autonomous Student Learning




Approaches to Teaching and Learning:
Recorded Audio Lectures
Tutorial/Review Sessions during the semester
Online discussion forums
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
COMP30030 - Introduction to AI, COMP30120 - Intro to Machine Learning, COMP41450 - Advanced Machine Learning, COMP47490 - Machine Learning (UG), COMP47990 - Machine Learning w Python (OL)

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Assignment 1 Unspecified n/a Graded No


Assignment: Assignment 2 Unspecified n/a Graded No


Examination: Final Exam 2 hour End of Trimester Exam No Alternative linear conversion grade scale 40% No


Carry forward of passed components
Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Tutorial Offering 1 Week(s) - 6, 10 Fri 13:00 - 14:20
Tutorial Offering 2 Week(s) - 6, 10 Fri 14:30 - 15:50