About the author

Hello! My name is Korbinian Strimmer and I am a Professor in Statistics. I am part of the Statistics group at the Department of Mathematics of the University of Manchester. You can find more information about me on my home page.

I have first taught this module in the winter term 2018 at the University of Manchester, and subsequently also in 2019, 2020, and 2021.

I hope you enjoy the course! If you have any questions, comments, or corrections then please email me at .

About the module

Topics covered

The MATH38161 module is designed to run over the course of 11 weeks. It has six parts, each covering a particular aspect of multivariate statistics and machine learning:

  1. Multivariate random variables and estimation in large and small sample settings (W1 and W2)
  2. Transformations and dimension reduction (W3 and W4)
  3. Unsupervised learning/clustering (W5 and W6)
  4. Supervised learning/classification (W7 and W8)
  5. Measuring and modelling multivariate dependencies (W9)
  6. Nonlinear and nonparametric models (W10, W11)

This module focuses on:

  • Concepts and methods (not on theory)
  • Implementation and application in R
  • Practical data analysis and interpretation (incl.┬áreport writing)
  • Modern tools in data science and statistics (R markdown, R studio)

Additional support material

Accompanying these notes are

Furthermore, there is also an MATH38161 online reading list hosted by the University of Manchester library.

If you are a University of Manchester student and enrolled in this module you will find on Blackboard:

  • a weekly learning plan for an 11 week study period,
  • weekly worksheets with with examples (theory and application in R) and solutions in R Markdown, and
  • exam papers of previous years.


Many thanks to Beatriz Costa Gomes for her help to compile the first draft of these course notes in the winter term 2018 while she was a graduate teaching assistant for this course. I also thank the many students who suggested corrections.