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CM50265: Machine learning 2

[Page last updated: 04 August 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: CW40EX60
Further information on unit assessment Assessment Detail:
  • Assessment detail data for this unit is currently being updated as a change has been approved. Updated assessment information will be published here shortly.
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Aims: This unit covers the breadth of machine learning topics as well as providing detailed treatment of advanced methods that are representative of the different categories of ML approaches.

Learning Outcomes: At the end of this unit, students will be able to:
* Demonstrate a systematic knowledge of state-of-the-art ML approaches and an awareness of the latest ongoing research in the field
* Develop and evaluate critically advanced ML models for real-world problems
* Identify and implement appropriate and original algorithms to perform inference
* Make predictions from models and account for uncertainty

Skills: Intellectual skills:
* Demonstrate an advanced conceptual understanding of ML modelling (T, F, A)
* Critical analysis of advanced models and algorithms (T, F, A)
Practical skills:
* Produce practical implementations of advanced ML algorithms (T, F, A)
* Evaluate and critique algorithms on complex data (T, F, A)
Transferable skills:
* Numerical programming and independent learning (F, A)
* Technical report writing and presentation skills (F, A)

Content: Topics covered will normally include: Bayesian approaches to ML, graphical models (e.g. Markov random fields), Bayesian non-parametric models (e.g. Gaussian processes), deep learning (e.g. neural networks), time series (e.g. hidden Markov models), sparse models (e.g. compressed sensing), and unsupervised learning (e.g. density estimation).

Programme availability:

CM50265 is Compulsory on the following programmes:

Department of Computer Science

CM50265 is Optional on the following programmes:

Department of Computer Science
  • RSCM-AFM51 : 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


  • 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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