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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 suitable machine learning approaches for different tasks, including state-of-the-art methods; 3) Run and evaluate a range of algorithms for different tasks using a standard machine learning toolkit; 4) Implement and evaluate machine learning algorithms in a high-level language.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Practical | 8 |
Autonomous Student Learning | 72 |
Total | 104 |
Either COMP47490 or COMP47460 is a prerequisite for this module.
To complete the continuous assessment, this module requires significant prior programming experience in a language such as Java, Python, Ruby or C/C++.
Students should have strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Continuous Assessment: Machine learning assignments. | Varies over the Trimester | n/a | Alternative linear conversion grade scale 40% | No | 60 |
Examination: End of Term Exam | 2 hour End of Trimester Exam | No | Alternative linear conversion grade scale 40% | No | 40 |
Resit In | Terminal Exam |
---|---|
Summer | No |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
feedback will be provided following assignment submission and correction.