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Curricular information is subject to change
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 Type | Hours |
---|---|
Lectures | 24 |
Computer Aided Lab | 10 |
Specified Learning Activities | 20 |
Autonomous Student Learning | 60 |
Total | 114 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
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 |
Resit In | Terminal Exam |
---|---|
Spring | Yes - 2 Hour |
• Feedback individually to students, post-assessment
Not yet recorded.
Name | Role |
---|---|
Ms Jiajing Li | Tutor |