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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, including state-of-the-art methods; 3) Run and evaluate the performance of a range of algorithms on real datasets using a standard machine learning toolkit; 4) Implement and evaluate machine learning algorithms in a high-level language.
Student Effort Type | Hours |
---|---|
Lectures | 20 |
Tutorial | 10 |
Autonomous Student Learning | 120 |
Total | 150 |
To complete the continuous assessment, this module requires significant prior programming experience in a language such as Java, Python, Ruby or C/C++.
Learning Recommendations:Students should have strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts.
Description | % of Final Grade | Timing |
---|---|---|
Examination: Final Exam | 55 |
3 hour End of Trimester Exam |
Continuous Assessment: Coursework | 45 |
Unspecified |
Compensation
This module is not passable by compensation
Resit Opportunities
No Resit
Remediation
If you fail this module you may repeat or substitute where permissible.