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EEEN40720

Academic Year 2025/2026

Machine Learning for Engineers (EEEN40720)

Subject:
Electronic & Electrical Eng
College:
Engineering & Architecture
School:
Electrical & Electronic Eng
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Emer Doheny
Trimester:
Autumn
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 engineering students with fundamental theoretical concepts in machine learning, including the mathematical concepts underlying a range of machine learning algorithms. Students will learn how to select, implement and evaluate an appropriate model for a given engineering task.
Key techniques in supervised machine learning will be covered, including classification using nearest neighbours, hyperplane and kernel algorithms, ensemble methods and neural networks. A particular emphasis will be placed on the application of these techniques to engineering, and on evaluation methods.
EEEN40720 requires strong mathematical ability, as some of the algorithms require an understanding of linear algebra and statistical concepts. Experience coding using Matlab or Python, and basic digital signal processing, is necessary for assignments.

About this Module

Learning Outcomes:

On successful completion of this module the student will be able to:
1) Identify a suitable machine learning algorithm for a given engineering task and dataset.
2) Understand the mathematical and statistical concepts underlying selected machine learning algorithms.
3) Understand the importance of applying appropriate model evaluation methods.
4) Demonstrate the ability to implement machine learning models (using Matlab or Python) using real engineering datasets (e.g. biomedical signals).

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

20
Autonomous Student Learning

60
Lectures

24
Computer Aided Lab

10
Total

114

Approaches to Teaching and Learning:
Lectures: Face-to-face lectures will be delivered, with content available online after each lecture.
Computer-aided laboratories: Practical labs will be performed individually or in groups. The module coordinator and teaching assistants will be on hand to assist during labs.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
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 In Module Component Repeat Offered
Assignment(Including Essay): Two assignments based on classification of signals using the algorithms and methods covered during lectures. Week 7, Week 10 Alternative linear conversion grade scale 40% No
35
 No
Exam (In-person): End of trimester exam. End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
50
 No
Quizzes/Short Exercises: Short multiple-choice quizzes during the trimester. Week 2, Week 4, Week 6, Week 8, Week 10 Alternative linear conversion grade scale 40% No
15
 No

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.

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