Statistical Learning - MA6529

Location Term Level Credits (ECTS) Current Convenor 2019-20
Canterbury Spring
View Timetable
6 15 (7.5) DR E Matechou


For delivery to students completing Stage 1 before September 2016:
Pre-requisite: MA306 (Statistics); MA323 (Probability and Matrices) or MA312 (Introduction to Financial Concepts); MA629 (Probability and Inference); MA632 (Regression Models)

For delivery to students completing Stage 1 after September 2016:
Pre-requisite: MAST4009 (Probability); MAST4011 (Statistics); MAST5007 (Mathematical Statistics) or MAST5001 (Applied Statistical Modelling 1)





Multivariate normal distribution, Inference from multivariate normal samples, principal component analysis, mixture models, factor analysis, clustering methods, discrimination and classification, graphical models, the use of appropriate software.


This module appears in:

Contact hours

36 hours

Method of assessment

80% examination, 20% coursework

Indicative reading

D. F. Morrision (1990). Multivariate Statistical Methods, McGraw-Hill Series in Probability and Statistics
T. Hastie, R. Tibshirani and J. H. Friedman (2009). The Elements of Statistical Learning, Spring-Verlag.
K. P. Murphy (2012). Machine Learning: A Probabilistic Perspective, MIT Press.

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes:
On successfully completing the level 6 module students will be able to:
1 demonstrate systematic understanding of key aspects of multivariate statistics and machine learning;
2 demonstrate the capability to deploy established approaches accurately to analyse and solve problems using a reasonable level of skill in calculation and manipulation of the material in the following areas: multivariate statistics, mixture modelling and clustering, discriminant analysis and graphical models;
3 apply key aspects of multivariate statistics and machine learning in well-defined contexts, showing judgement in the selection and application of tools and techniques;
4 show judgement in the application of R.

The intended generic learning outcomes:
On successfully completing the level 6 module students will be able to:
1 manage their own learning and make use of appropriate resources;
2 understand logical arguments, identifying the assumptions made and the conclusions drawn;
3 communicate straightforward arguments and conclusions reasonably accurately and clearly;
4 manage their time and use their organisational skills to plan and implement efficient and effective modes of working;
5 solve problems relating to qualitative and quantitative information;
6 make competent use of information technology skills such as online resources (moodle), internet communication;
7 communicate technical and non-technical material competently;
8 demonstrate an increased level of skill in numeracy and computation;
9 demonstrate the acquisition of the study skills needed for continuing professional development.

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