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![]() | 2016/7 |
![]() | Department of Computer Science |
![]() | 12 [equivalent to 24 CATS credits] |
![]() | 240 |
![]() | Masters UG & PG (FHEQ level 7) |
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![]() | CW 100% |
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![]() | Undergraduate students selecting this unit should note the teaching structure |
![]() | 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. 4. Evaluate and recommend suitable techniques for a given problem. 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. |
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CM50246 is Compulsory on the following programmes:Department of Computer Science
CM50246 is Optional on the following programmes:Department of Computer Science
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Notes:
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