Find your future
at our Open Days
Book nowat our Open Days
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
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.
University of Kent makes every effort to ensure that module information is accurate for the relevant academic session and to provide educational services as described. However, courses, services and other matters may be subject to change. Please read our full disclaimer.