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CM30322: Bayesian machine learning

[Page last updated: 02 August 2022]

Academic Year: 2022/23
Owning Department/School: Department of Computer Science
Credits: 6 [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Honours (FHEQ level 6)
Semester 2
Assessment Summary: CW60EX40
Further information on unit assessment Assessment Detail:
  • Assessment detail for this unit will be available shortly.
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
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.

Programme availability:

CM30322 is Optional on the following programmes:

Department of Computer Science
  • USCM-AFB27 : BSc(Hons) Computer Science and Artificial Intelligence (Year 3)
  • USCM-AFM27 : MComp(Hons) Computer Science and Artificial Intelligence (Year 3)
  • USCM-AAM27 : MComp(Hons) Computer Science and Artificial Intelligence with Study year abroad (Year 3)
  • USCM-AKM27 : MComp(Hons) Computer Science and Artificial Intelligence with Year long work placement (Year 3)


  • This unit catalogue is applicable for the 2022/23 academic year only. Students continuing their studies into 2023/24 and beyond should not assume that this unit will be available in future years in the format displayed here for 2022/23.
  • Programmes 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.