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COMP47490

Academic Year 2026/2027

Machine Learning (UG) (COMP47490)

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

Curricular information is subject to change.

This module is primarily meant for UG students.

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.

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 using various evaluation metrics; 4) be able to use Python and scikit-learn for machine learning tasks using real datasets.

Indicative Module Content:

Classification Techniques including kNN, Decision Trees, Naive Bayes and SVM
Regression
Gradient Descent and Neural Networks
Ensembles
Evaluation Methodology and Measures
Introduction to Reinforcement Learning
Unsupervised learning techniques including dimensionality reduction, partitional and hierarchical clustering
Running machine learning tasks using Python/Scikit-learn

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

24
Tutorial

24
Autonomous Student Learning

80
Total

128

Approaches to Teaching and Learning:
Practical Labs; Problem-based learning;

Students are NOT permitted to use generative AI tools in assessments, unless explicitly indicated in the assessment text.

Requirements, Exclusions and Recommendations
Learning Requirements:

To succeed in COMP 47490, students should normally have passed COMP 30030 ("Introduction to AI"). If a student has not successfully completed this module, then they should consult the Module Coordinator before registering

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, COMP47460 - Machine Learning (Blended Del), COMP47750 - Machine Learning with Python, COMP47990 - Machine Learning w Python (OL)


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade Component repeat (in-module) Offered
Practical Skills Assessment: A computer-based assessment that will require students to answer questions, perform calculations, and write/debug programs. This will be conducted during the practical timeslot. Week 6, Week 10 Alternative linear conversion grade scale 40% No
30
 No
Exam (In-person): End of term 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
No
 

Resit In Terminal Exam
Spring No
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.

"Machine Learning" by Tom Mitchell
"Fundamentals of Machine Learning for Predictive Data Analytics" by John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
"Machine Learning: The Art and Science of Algorithms that Make Sense of Data" by Peter Flach
"The Elements of Statistical Learning: Data Mining, Inference and Prediction" by Trevor Hastie, Robert Tibshirani and Jerome Friedman

Name Role
Priscilla Adong Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Mon 09:00 - 09:50
Autumn Practical Offering 1 Week(s) - Autumn: All Weeks Mon 12:00 - 13:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Wed 12:00 - 12:50
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