Background material: multivariate normal distribution, inference from multivariate normal samples
Indicative module content:
• Principal component and factor analysis, latent variable model, clustering and classification methods
• Likelihood-based analysis such as maximum likelihood, EM algorithm, optimisation, confidence interval construction
• Simulation and sampling methods, bootstrap, permutation tests
• Model building including tests such as the Wald test
• R programming including real-world applications in areas such as biology, ecology, sociology and economics to data that does not always follow standard statistical models.
Total contact hours: 36
Private study hours: 114
Total study hours: 150
Method of assessment
Assessment 1 Exercises, requiring on average between 10 and 15 hours to complete 20%
Assessment 2 Exercises, requiring on average between 10 and 15 hours to complete 20%
At least one assessment will include the use of R.
Examination 2 hours 60%
The coursework mark alone will not be sufficient to demonstrate the student's level of achievement on the module.
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.
Morgan, B. J. T. (2009) Applied stochastic modelling, Chapman and Hall.
On successfully completing this module students will be able to:
1) demonstrate systematic understanding of key aspects of multivariate and computational 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, clustering (e.g., mixture modelling), classification, machine learning methods (e.g. graphical models) , maximum likelihood estimation, the EM algorithm and simulation methods
3) apply key aspects of multivariate and computational 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.
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Credit level 6. Higher level module usually taken in Stage 3 of an undergraduate degree.
- ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
- The named convenor is the convenor for the current academic session.
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