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COMP47460

Academic Year 2026/2027

Machine Learning (Blended Delivery) (COMP47460)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Aonghus Lawlor
Trimester:
Autumn
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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.

About this Module

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.

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


 

Student Effort Hours:
Student Effort Type Hours
Lectures

16
Tutorial

8
Practical

4
Autonomous Student Learning

80
Total

108

Approaches to Teaching and Learning:
Recorded Audio Lectures
Tutorial/Review Sessions during the semester
Online discussion forums
Tutorials
Assignments

If students are permitted to use generative AI tools in assignments, that will be indicated in the assignment specification.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
COMP30030 - Introduction to AI, COMP30120 - Intro to Machine Learning, COMP41450 - Advanced Machine Learning, COMP47490 - Machine Learning (UG), COMP47750 - Machine Learning with Python, COMP47990 - Machine Learning w Python (OL), EEEN40720 - Machine Learning for Engineers


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade Component repeat (in-module) Offered
Assignment(Including Essay): Report 1 on Machine Learning analysis of current dataset Week 7 Pass/Fail Grade Scale No
15
 No
Assignment(Including Essay): Report 2 on Machine Learning analysis of a current dataset Week 11 Pass/Fail Grade Scale No
15
 No
Exam (In-person): In person end of semester exam End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
70
 No

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.


Carry forward of passed components
Yes
 

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.
Autumn Tutorial Offering 1 Week(s) - 6, 10 Fri 13:00 - 14:50
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