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Curricular information is subject to change
On completion of this module, students will be able to:
1) Distinguish between the different categories of machine learning algorithms;
2) Identify a suitable machine learning algorithm for a given application or task;
3) Run and evaluate the performance of a range of algorithms on real datasets using Python libraries.
Student Effort Type | Hours |
---|---|
Autonomous Student Learning | 86 |
Lectures | 14 |
Practical | 10 |
Total | 110 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: Machine Learning Exercise | Week 9 | n/a | Alternative linear conversion grade scale 40% | No | 20 |
Examination: End of semester exam | 1 hour End of Trimester Exam | No | Alternative linear conversion grade scale 40% | No | 60 |
Assignment: Machine Learning exercise | Week 6 | n/a | Alternative linear conversion grade scale 40% | No | 20 |
Remediation Type | Remediation Timing |
---|---|
Repeat | Within Two Trimesters |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
Name | Role |
---|---|
Bahavathy Kathirgamanathan | Tutor |
Lecture | Offering 1 | Week(s) - Autumn: All Weeks | Thurs 10:00 - 10:50 |
Lecture | Offering 1 | Week(s) - Autumn: All Weeks | Wed 12:00 - 12:50 |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 30, 31, 32, 33 | Thurs 10:00 - 10:50 |
Lecture | Offering 1 | Week(s) - 29 | Thurs 10:00 - 10:50 |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Tues 15:00 - 15:50 |