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CM50265: Machine learning 2

[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: Masters UG & PG (FHEQ level 7)
Period:
Semester 2
Assessment Summary: CW 40%, EX 60%
Assessment Detail:
  • Coursework (CW 40%)
  • Examination (EX 60%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites:
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

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.

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
  • 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
  • USCM-AFM01 : MComp(Hons) Computer Science (Year 4)
  • USCM-AAM02 : MComp(Hons) Computer Science with Study year abroad (Year 5)
  • USCM-AKM02 : MComp(Hons) Computer Science with Year long work placement (Year 5)
  • USCM-AFM14 : MComp(Hons) Computer Science and Mathematics (Year 4)
  • USCM-AAM14 : MComp(Hons) Computer Science and Mathematics with Study year abroad (Year 5)
  • USCM-AKM14 : MComp(Hons) Computer Science and Mathematics with Year long work placement (Year 5)

Notes:

  • 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.