MA50303: Advanced topics in machine learning and mathematical modelling
[Page last updated: 09 August 2024]
| Academic Year: | 2024/25 |
| Owning Department/School: | Department of Mathematical Sciences |
| Credits: | 6 [equivalent to 12 CATS credits] |
| Notional Study Hours: | 120 |
| Level: | Masters UG & PG (FHEQ level 7) |
| Period: |
- Semester 2
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| Assessment Summary: | CWOI 25%, CWRI 75% |
| Assessment Detail: |
- Report (CWRI 75%)
- Oral Presentation (CWOI 25%)
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| Supplementary Assessment: |
- Like-for-like reassessment (where allowed by programme regulations)
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| Requisites: |
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| Learning Outcomes: |
After taking this course students should be able to:
- Demonstrate knowledge of modern machine learning techniques
- Identify problems in applied mathematics which can be approached with machine learning
- Use computational tools for applying machine learning
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| Synopsis: | Introduce students to key modern machine learning in the context of statistical applied mathematics.
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| Content: | Under the guidance of the unit convenor, students will select topics from recent research articles to study in more details including:
- Solving differential equations with neural networks
- Regularising inverse problems with neural networks
- Designing neural network architectures based on differential equations
- Prediction with Gaussian processes
- Universal approximation of neural networks
- Generative models with score-based diffusion
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| Course availability: |
MA50303 is Compulsory on the following courses:
Department of Mathematical Sciences
- RSMA-AFM18 : Integrated PhD Statistical Applied Mathematics
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Notes: - This unit catalogue is applicable for the 2024/25 academic year only. Students continuing their studies into 2025/26 and beyond should not assume that this unit will be available in future years in the format displayed here for 2024/25.
- Courses and units are subject to change in accordance with normal University procedures.
- Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
- Find out more about these and other important University terms and conditions here.
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