EEEN40720 Machine Learning for Engineers

Academic Year 2023/2024

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: Applying machine learning methods to existing data to address an engineering problem. This will involves writing code and a report. Unspecified n/a Standard conversion grade scale 40% No

30
Continuous Assessment: Short quizzes will be conducted throughout the trimester to examine specific topics. The may comprise multiple choice or short exam questions. Throughout the Trimester n/a Alternative linear conversion grade scale 40% No

20
Examination: Final exam. 2 hour End of Trimester Exam No 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
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 10:00 - 10:50
Computer Aided Lab Offering 1 Week(s) - 3, 4, 6, 7, 10, 12 Fri 15:00 - 16:50
Lecture Offering 1 Week(s) - Autumn: All Weeks Tues 14:00 - 14:50
Autumn