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CM50264: Machine learning 1

[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)
Period:
Semester 1
Assessment Summary: CW30EX70
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)
Requisites: While taking this module you must take XX50215
Aims: This unit provides the core of machine learning (ML) by presenting algorithmic approaches to ML as well as an introduction to more advanced topics such as probabilistic techniques.

Learning Outcomes: At the end of this unit, students will be able to:
* Display a systematic knowledge of algorithmic ML approaches and demonstrate a comprehensive understanding of their application to specific, real-world problems
* Produce practical implementations of algorithmic ML approaches
* Evaluate critically the relative merits and limitations of algorithmic ML approaches

Skills: Intellectual skills:
* Identify and discriminate between modelling problems (T, F, A)
* Critical analysis of algorithms (T, F, A)
Practical skills:
* Produce practical implementations of algorithms (T, F, A)
* Evaluate algorithms on real data (T, F, A)
Transferable skills:
* Numerical programming and independent learning (F, A)
* Technical report writing (F, A)

Content: Topics covered will normally include: numerical optimisation for parameter estimation; algorithmic unsupervised learning (e.g. k-mean clustering and principal component analysis); discriminative approaches to classification and regression; fundamental parametric linear models (e.g. generalised linear models), parametric non-linear models (e.g. decision trees), non-parametric models (e.g. k-nearest neighbours), and ensemble approaches (e.g. boosting).

Programme availability:

CM50264 is Compulsory on the following programmes:

Department of Computer Science Department of Mathematical Sciences
  • TSMA-AFM19 : MSc Mathematics with Data Science for Industry
  • TSMA-AWM19 : MSc Mathematics with Data Science for Industry

CM50264 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
  • 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 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.
  • Find out more about these and other important University terms and conditions here.