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CM32024: 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: Honours (FHEQ level 6)
Period:
Academic Year
Assessment Summary: CWRG 100%
Assessment Detail:
  • Bayesian Computation Coursework (CWRG 50%)
  • Bayesian Computation Coursework (CWRG 50%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: In taking this module you cannot take CM52037
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.


Synopsis: You will explore the philosophy, theory, and practice of Bayesian inference, and its general relevance in machine learning. You will apply numerical computation methods and key algorithms to implement Bayesian modelling to solve problems in machine learning, drawing in part from existing software libraries.

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:

CM32024 is Optional on the following courses:

Department of Computer Science
  • USCM-AFB31 : BSc(Hons) Computer Science and Artificial Intelligence (Year 3)
  • USCM-AAB27 : BSc(Hons) Computer Science and Artificial Intelligence with Study year abroad (Year 4)
  • USCM-AKB27 : BSc(Hons) Computer Science and Artificial Intelligence with Year long work placement (Year 4)
  • USCM-AFM31 : MComp(Hons) Computer Science and Artificial Intelligence (Year 3)
  • USCM-AKM31 : MComp(Hons) Computer Science and Artificial Intelligence with professional placement (Year 3)
  • USCM-AKM31 : MComp(Hons) Computer Science and Artificial Intelligence with study abroad (Year 3)

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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