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CM50268: Bayesian machine learning

[Page last updated: 18 October 2021]

Academic Year: 2021/2
Owning Department/School: Department of Computer Science
Credits: 6 [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Masters UG & PG (FHEQ level 7)
Semester 2
Assessment Summary: CW 100%
Assessment Detail:
  • Written Report on Case Study (CW 70%)
  • Completed Lab Reports (CW 30%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Aims: To convey an appreciation of the philosophy and practical features of Bayesian inference, its general relevance in data science, along with key algorithms and methods of implementation in both a generic and a machine learning context.

Learning Outcomes: After completion of the unit, students should be able to:
* explain the philosophical foundations of Bayesian inference, and critically review the advantages and disadvantages of the paradigm,
* apply and quantitatively assess approximation methods for Bayesian inference,
* critically evaluate a range of Bayesian modelling techniques within machine learning scenarios,
* implement a baseline Bayesian model (e.g. linear regression) in a relevant programming language (e.g. Python),
* employ more advanced Bayesian software libraries to solve problems in data science.

Skills: Intellectual skills:
* Conceptual understanding of Bayesian inference (T,F,A)
* Critical appreciation of probabilistic modelling paradigms (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)
* Technical report writing (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 and elsewhere, stochastic and deterministic approximation methods, specific Bayesian treatments of linear models, neural networks and Gaussian processes etc.

Programme availability:

CM50268 is Optional on the following programmes:

Department of Computer Science
  • RSCM-AFM51 : Integrated PhD Accountable, Responsible and Transparent Artificial Intelligence
  • RSCM-APM51 : Integrated PhD Accountable, Responsible and Transparent Artificial Intelligence
  • TSCM-AFM51 : MRes Accountable, Responsible and Transparent Artificial Intelligence
  • TSCM-AFM52 : MSc Accountable, Responsible and Transparent Artificial Intelligence
  • TSCM-AFM45 : MSc Data Science
  • TSCM-AWM45 : MSc Data Science
  • TSCM-AFM48 : MSc Machine Learning and Autonomous Systems
  • TSCM-AWM48 : MSc Machine Learning and Autonomous Systems


  • This unit catalogue is applicable for the 2021/22 academic year only. Students continuing their studies into 2022/23 and beyond should not assume that this unit will be available in future years in the format displayed here for 2021/22.
  • 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.
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