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CM52037: Bayesian machine learning

[Page last updated: 28 April 2025]

Academic Year: 2025/26
Owning Department/School: Department of Computer Science
Credits: 10 [equivalent to 20 CATS credits]
Notional Study Hours: 200
Level: Masters UG & PG (FHEQ level 7)
Period:
Academic Year
Assessment Summary: CWRG 100%
Assessment Detail:
  • Bayesian Computation Project (CWRG 50%)
  • Bayesian Computation Project (CWRG 50%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: In taking this module you cannot take CM32024 OR take CM50268 OR take CM30322
Before taking this module you must take CM22009 OR 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. Perform Bayesian modelling for simple toy problems from statistical perspective. 4. Implement a baseline Bayesian model (e.g. linear regression) in a relevant programming language (e.g. Python). 5. Employ and combine Bayesian computation libraries with other established machine learning packages (e.g. pandas, numpy) to solve problems in machine learning. 6. Critically evaluate the relative strengths and weaknesses of Bayesian inference methods.


Synopsis: To convey an appreciation of the philosophy and practical features of Bayesian inference, its general relevance in machine learning, numerical computation methods, along with key algorithms and methods of implementation.

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, basic stochastic and deterministic approximation methods, specific Bayesian treatments of linear models.

Course availability:

CM52037 is Optional on the following courses:

Department of Computer Science
  • USCM-AFM27 : MComp(Hons) Computer Science and Artificial Intelligence (Year 4)
  • USCM-AAM27 : MComp(Hons) Computer Science and Artificial Intelligence with Study year abroad (Year 5)
  • USCM-AKM27 : MComp(Hons) Computer Science and Artificial Intelligence with Year long work placement (Year 5)

Notes:

  • This unit catalogue is applicable for the 2025/26 academic year only. Students continuing their studies into 2026/27 and beyond should not assume that this unit will be available in future years in the format displayed here for 2025/26.
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