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COMP47750

Academic Year 2024/2025

Machine Learning with Python (COMP47750)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Professor Pádraig Cunningham
Trimester:
Autumn and Spring (separate)
Mode of Delivery:
On Campus
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 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 covered include ensemble learning, dimension reduction, and model selection. This module requires strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts. Exercises and assignments will use the machine learning libraries in Python.

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 Python libraries.

Student Effort Hours:
Student Effort Type Hours
Lectures

14
Practical

10
Autonomous Student Learning

86
Total

110

Approaches to Teaching and Learning:
Learning theoretical concepts in lectures.
Learning practical skills through assignments.

Requirements, Exclusions and Recommendations

Not applicable to this module.


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


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Machine Learning Exercise 1 Week 7 Alternative linear conversion grade scale 40% No
20
 No
Assignment(Including Essay): Machine Learning Exercise 2 Week 11 Alternative linear conversion grade scale 40% No
20
 No
Exam (In-person): End of Trimester Exam End of trimester
Duration:
1 hr(s)
 Alternative linear conversion grade scale 40% No
60
 No

Carry forward of passed components
No
 

Remediation Type Remediation Timing
Repeat Within Two Trimesters
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Oshana Iddi Dissanayake 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) - Autumn: All Weeks Thurs 10:00 - 10:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Wed 12:00 - 12:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 10:00 - 10:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 12:00 - 12:50
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