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CM50264: Machine learning 1

Follow this link for further information on academic years Academic Year: 2017/8
Further information on owning departmentsOwning Department/School: Department of Computer Science
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 1
Further information on unit assessment Assessment Summary: CW40PR60
Further information on unit assessment Assessment Detail:
  • Assessment detail to be confirmed
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites: While taking this module you must take XX50215
Further information on descriptions Description: 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).
Further information on programme availabilityProgramme availability:

CM50264 is Compulsory on the following programmes:

Department of Computer Science

CM50264 is Optional on the following programmes:

Department of Computer Science
  • 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: