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 |
|---|---|
| Autonomous Student Learning | 72 |
| Lectures | 24 |
| Practical | 8 |
| Total | 104 |
Having previously completed an introductory machine learning module (such as COMP47490, COMP47460, or COMP47750) is a prerequisite for this module.
To complete the continuous assessment, this module requires significant prior programming experience, ideally in Python.
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 | ||
|---|---|---|---|---|---|
| Exam (In-person): End of year final exam. | End of trimester Duration: 2 hr(s) |
Alternative linear conversion grade scale 40% | No | 40 |
No |
| Individual Project: Project involving development of machine learning solutions. May be completed in small groups or individually. | Week 6 | Alternative linear conversion grade scale 40% | No | 26 |
No |
| Individual Project: Project involving development of machine learning solutions. May be completed in small groups or individually. | Week 11 | Alternative linear conversion grade scale 40% | No | 26 |
No |
| Participation in Learning Activities: Participation in ungraded, but mandatory, in-class development, reading group, and other activities. | Week 2, Week 4, Week 6, Week 8, Week 10 | Alternative linear conversion grade scale 40% | No | 8 |
No |
| 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.
| Spring | Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 33 | Fri 12:00 - 13:50 |
| Spring | Laboratory | Offering 1 | Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Tues 15:00 - 15:50 |