
Academic Year:  2017/8 
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: 

Assessment Summary:  CW 100% 
Assessment Detail: 

Supplementary Assessment: 

Requisites:  While taking this module you must take XX50215 
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, realworld 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. kmean clustering and principal component analysis); discriminative approaches to classification and regression; fundamental parametric linear models (e.g. generalised linear models), parametric nonlinear models (e.g. decision trees), nonparametric models (e.g. knearest neighbours), and ensemble approaches (e.g. boosting). 
Programme availability: 
CM50264 is Compulsory on the following programmes:Department of Computer Science
CM50264 is Optional on the following programmes:Department of Computer Science

Notes:
