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CM50246: Machine learning and AI

Follow this link for further information on academic years Academic Year: 2014/5
Further information on owning departmentsOwning Department/School: Department of Computer Science
Further information on credits Credits: 6
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: CW 100%
Further information on unit assessment Assessment Detail:
  • CW1 (CW 100%)
Further information on supplementary assessment Supplementary Assessment: Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites:
Further information on descriptions Description: Aims:
To understand the fundamentals of Machine Learning and be equipped with the skills needed to specify and undertake an independent project.

Learning Outcomes:
Students will be able to:
1. Use probabilistic modelling techniques to represent real-world problems.
2. Apply statistical inference to solve these problems in a principled fashion.
3. Use the techniques of machine learning in the specific context of vision or graphics applications.

Skills:
Probability and Statistics (tfa), Linear Algebra (tfa), Programming and Experiment (tfa).

Content:
Probability and Statistics Fundamentals, Regression, Classification, Clustering, Kernel Methods, Nearest Neighbours, Linear Models, Logistic Regression, Support Vector Machines, Markov Models, Sampling and MCMC, Mixture Models, Decision Trees.
Further information on programme availabilityProgramme availability:

CM50246 is Compulsory on the following programmes:

Department of Computer Science
Notes:
* This unit catalogue is applicable for the 2014/15 academic year only. Students continuing their studies into 2015/16 and beyond should not assume that this unit will be available in future years in the format displayed here for 2014/15.
* Programmes and units are subject to change at any time, 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.