CM20220: Fundamentals of machine learning
[Page last updated: 27 October 2020]
Academic Year:  2020/1 
Owning Department/School:  Department of Computer Science 
Credits:  6 [equivalent to 12 CATS credits] 
Notional Study Hours:  120 
Level:  Intermediate (FHEQ level 5) 
Period: 

Assessment Summary:  CW 25%, EX 75% 
Assessment Detail: 

Supplementary Assessment: 

Requisites:  Before taking this module you must take CM20219 
Description:  Aims: To introduce techniques of Pattern Analysis and Statistical Machine Learning. To enable students to model real phenomena using statistical models and probability. To develop understanding of inference techniques and to equip students with the necessary numerical computing techniques to deploy them in practice. Learning Outcomes: On completion of this unit students will be able to: 1. Develop probabilistic models of real world phenomena 2. Use inference procedures and deploy relevant optimisation techniques 3. Use appropriate statistical and numerical methods in applications Skills: Problem Solving (T/F,A), Application of Number (T/F,A). Content: Probability and Statistics * Probability Theory, Probability Distributions, Frequency, Bayesian Statistics Inference and Numerical Methods * Regression, Classification, Least Squares, Bayesian Estimation, Optimisation, Matrix Decomposition Modelling and Applications * Feature Spaces, Clustering, Density Estimation. 
Programme availability: 
CM20220 is Compulsory on the following programmes:Department of Computer Science
CM20220 is Optional on the following programmes:Department of Computer Science

Notes:
