Statistical Learning for Data Scientists - MAST6053

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Module delivery information

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.


Contact hours

Total contact hours: 36
Private study hours: 114
Total study hours: 150

Method of assessment

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

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.
Morgan, B. J. T. (2009) Applied stochastic modelling, Chapman and Hall.

Learning outcomes

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.


  1. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  2. The named convenor is the convenor for the current academic session.
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