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Curricular information is subject to change
Learning how to work on a significant project that requires combination of acquired computational, statistical, and mathematical skills.
Indicative Module Content:(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 | ||
---|---|---|---|---|---|
Project: Supplementary material for the project such as codes, data files etc. All to be uploaded to a shared GitHub repository with a README included. | Coursework (End of Trimester) | n/a | Standard conversion grade scale 40% | Yes | 20 |
Essay: Brief literature review for selected project to be given within the first month of the trimester. This is to be uploaded to Brightspace and is marked based on completion. | Week 4 | n/a | Standard conversion grade scale 40% | No | 5 |
Presentation: Poster presentation of the project. To be given in the last week of the Trimester. | Coursework (End of Trimester) | n/a | Standard conversion grade scale 40% | Yes | 75 |
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.