COMP47490 Machine Learning

Academic Year 2020/2021

The objective of this module is to familiarise students with the fundamental theoretical concepts in machine learning, as well as to instruct 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 and applications of machine learning will also be introduced. 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
Lectures

14
Tutorial

10
Autonomous Student Learning

80
Total

104
Approaches to Teaching and Learning:
Practical Labs; Continuous Assessment; enquiry & problem-based learning; 
Requirements, Exclusions and Recommendations
Learning Recommendations:

This module requires strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts.


Module Requisites and Incompatibles
Incompatibles:
COMP30120 - Intro to Machine Learning, COMP41450 - Advanced Machine Learning, COMP47460 - Machine Learning (Blended Del)


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: A number of Brightspace quizzes to test the understanding of theoretical and application concepts Throughout the Trimester n/a Graded No

20
Assignment: Assignment 2: Applying ML techniques in some simplified real-world application Unspecified n/a Graded No

40
Assignment: Assignment 1: Applying ML techniques in some simplified real-world application Unspecified n/a Graded No

40

Carry forward of passed components
No
 
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.

Name Role
Dr Deepak Ajwani Lecturer / Co-Lecturer