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EEEN40720

Academic Year 2024/2025

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 an appropriate model and understand the underlying algorithm, through engineering-based examples.
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.
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) Using Matlab or Python, implement machine learning tasks 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
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
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
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

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.

Name Role
Abu Shakil Ahmed Tutor
Felix Jarto 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 Fri 14:00 - 14:50
Autumn Computer Aided Lab Offering 1 Week(s) - 3, 4, 6, 7, 10, 12 Fri 15:00 - 16:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Tues 14:00 - 14:50
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