Curricular information is subject to change.
Learning how to work on a significant project that requires combination of acquired computational, statistical, and mathematical skills.
(Pseudo)random number generators, Monte Carlo simulations, machine learning.
| Student Effort Type | Hours |
|---|---|
| Lectures | 6 |
| Autonomous Student Learning | 300 |
| Total | 306 |
Not applicable to this module.
| Description | Timing | Component Scale | % of Final Grade | ||
|---|---|---|---|---|---|
| Assignment(Including Essay): Brief literature review for your chosen project due the end of week 4. | Week 4 | Other | No | 5 |
No |
| Individual Project: Poster presentation of your project to take place during the summer exam week. Must be in person. Posters should be A0 size. You can also simply tape A4 pages together. | Week 14, Week 15 | Other | No | 50 |
No |
| Individual Project: README files on your git repository that explain the project and give code/library installation/run instructions. It's due the very last exam day of the summer term. | Week 14, Week 15 | Other | No | 30 |
No |
| Individual Project: Codes necessary to reproduce the results of your project. Should be well documented and uploaded into your github repository where your README is (see previous component). Due the last day of exams. | Week 14, Week 15 | Other | No | 15 |
No |
| Remediation Type | Remediation Timing |
|---|---|
| In-Module Resit | Prior to relevant Programme Exam Board |
• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
Feedback will be given on the final poster presentation.