EEEN40720 Machine Learning for Engineers

Academic Year 2024/2025

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 algorithm, 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 some understanding of linear algebra and statistical concepts.

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Curricular information is subject to change

Learning Outcomes:

On successful completion of this module the student will be able to:
1) Distinguish between the different categories of machine learning algorithms.
2) Understand the mathematical and statistical concepts underlying selected machine learning algorithms.
3) Identify a suitable machine learning algorithm for a given engineering task.
4) Use Matlab or Python for machine learning tasks using real engineering datasets (e.g. biomedical signals).

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Computer Aided Lab

10
Specified Learning Activities

20
Autonomous Student Learning

60
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 pairs. 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 Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment(Including Essay): Two assignments based on classification of signals using the algorithms and methods covered during lectures. n/a Alternative linear conversion grade scale 40% No

35
Quizzes/Short Exercises: Short multiple-choice quizzes during the trimester. n/a Alternative linear conversion grade scale 40% No

15
Exam (In-person): End of trimester exam. n/a Alternative linear conversion grade scale 40% No

50

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
Ms Jiajing Li Tutor