CM30322: Bayesian machine learning
[Page last updated: 23 October 2023]
| Academic Year: | 2023/24 |
| Owning Department/School: | Department of Computer Science |
| Credits: | 6 [equivalent to 12 CATS credits] |
| Notional Study Hours: | 120 |
| Level: | Honours (FHEQ level 6) |
| Period: |
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| Assessment Summary: | CW 100% |
| Assessment Detail: |
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| Supplementary Assessment: |
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| Requisites: | Before taking this module you must take CM20315 |
| Learning Outcomes: |
After completion of the unit, students should be able to:
1. explain the philosophical and mathematical foundations of Bayesian inference, 2. apply and quantitatively assess approximation methods for Bayesian inference, 3. implement a baseline Bayesian model (e.g. linear regression) in a relevant programming language (e.g. Python), 4. employ more advanced Bayesian software libraries to solve problems in machine learning. |
| Aims: | To convey an appreciation of the philosophy and practical features of Bayesian inference, its general relevance in machine learning, along with key algorithms and methods of implementation. |
| Skills: | Intellectual skills:
* Conceptual understanding of Bayesian inference (T,F,A) Practical skills: * Programming Bayesian analytic algorithms (T,F,A) * Use of software packages for Bayesian modelling (T,F,A) Transferable skills: * Numerical programming (F,A) |
| Content: | Topics covered by this unit will typically include the history and philosophy of Bayesian inference, key concepts such as priors, marginalisation and Occam's razor, practical Bayesian methodology in machine learning contexts, stochastic and deterministic approximation methods, specific Bayesian treatments of linear models, neural networks and Gaussian processes. |
| Course availability: |
CM30322 is Optional on the following courses:Department of Computer Science
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Notes:
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