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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 a standard machine learning toolkit.
Student Effort Type | Hours |
---|---|
Lectures | 16 |
Tutorial | 8 |
Practical | 4 |
Autonomous Student Learning | 80 |
Total | 108 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: Assignment 1 | Unspecified | n/a | Graded | No | 20 |
Assignment: Assignment 2 | Unspecified | n/a | Graded | No | 20 |
Examination: Final Exam | 2 hour End of Trimester Exam | No | Alternative linear conversion grade scale 40% | No | 60 |
Resit In | Terminal Exam |
---|---|
Spring | Yes - 2 Hour |
• Feedback individually to students, post-assessment
Not yet recorded.