This module is not currently running in 2024 to 2025.
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.
In addition, for level 7 students: advanced EM algorithm methods, advanced simulation methods, writing R programs for advanced methods and applications.
Total contact hours: 36
Private study hours: 114
Total study hours: 150
Level 6
Assessment 1 (10-15 hrs) 20%
Assessment 2 (10-15 hrs) 20%
Examination (2 hours) 60%
Level 7
Assessment 1 (10-15 hrs) 20%
Assessment 2 (10-15 hrs) 20%
Examination (2 hours) 60%
Reassessment methods
Like-for-like
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 the level 7 module students will be able to:
1. demonstrate systematic understanding of multivariate and computational statistics and machine learning;
2. demonstrate the capability to solve complex problems using a very good 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 such as graphical models , maximum likelihood estimation, the EM algorithm and simulation methods
3. apply a range of concepts and principles in multivariate and computational statistics and machine learning in loosely defined contexts, showing good judgment in the selection and application of tools and techniques;
4. make effective and well-considered use of R.
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