|
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, real-world 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. k-mean clustering and principal component analysis); discriminative approaches to classification and regression; fundamental parametric linear models (e.g. generalised linear models), parametric non-linear models (e.g. decision trees), non-parametric models (e.g. k-nearest 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:
|