Statistical Learning for Data Scientists - MAST7053

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2022 to 2023
Canterbury
Spring Term 7 15 (7.5) checkmark-circle

Overview

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.

Details

Contact hours

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

Method of assessment

20% Coursework
80% Exam

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 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.

Notes

  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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