CM50264: Machine learning 1
[Page last updated: 23 October 2023]
Academic Year:  2023/24 
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 30%, EX 70% 
Assessment Detail: 

Supplementary Assessment: 

Requisites:  
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 
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. 
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). 
Course availability: 
CM50264 is Compulsory on the following courses:Department of Computer Science
CM50264 is Optional on the following courses:Department of Computer Science

Notes:
