CM50264: Machine learning 1
[Page last updated: 18 October 2021]
Academic Year: | 2021/2 |
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: |
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Assessment Summary: | CW 30%, EX 70% |
Assessment Detail: |
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Supplementary Assessment: |
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Requisites: | While taking this module you must take XX50215 |
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
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Notes:
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