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Repository on Github:


This course offers an introductory and applied overview of “supervised” Machine Learning. This includes:

  • ML Basics
  • Supervised Regression
  • Supervised Classification
  • Performance Evaluation
  • Classification and Regression Trees
  • Random Forests
  • Parameter Tuning
  • Practical Advice

The course is of an introductory nature and geared towards students with some statistics background. It is aimed at a practical and operational understanding of the covered algorithms and models, with less emphasis on theory and formalism. The accompanying exercises, demos and tutorials are a mix of theoretical and p ractical assignments, the latter in R (mostly with mlr3).


The course is organized as a digital lecture, which should be as self-contained and enable self-study as much as possible. The major part of the material is provided as slide sets with lecture videos. We have also prepared interactive tutorials where you can answer multiple choice questions, and learn how to apply the covered methods in R on some short coding exercises. Our plan is to extend this self-study material over the next months and years.


The course is targeted at ML beginners with a basic, university level, education in maths and statistics:

  • Basic linear algebra: vectors, matrices, determinants
  • Simple calculus: derivatives, integrals, gradients
  • Some probability theory: probability, random variables, distributions
  • Basic statistics knowledge: descriptive statistics, estimators.
    (Linear) modelling from a statistics perspective will help, but is not required.
  • Working knowledge of R

Help is appreciated and welcome!

We hope to continously improve and expand this course over the coming years. We strongly believe in open source and collaborative work. Please contact us if you think likewise and would like to contribute. See also our contributing guidelines

  • Are you an ML expert and like the course, but have some feedback or consider extending it? Write an email to Bernd and Fabian (see Team page) or Open an issue.
  • Are you a student taking the lecture - either at the LMU or online - and you spotted a typo, think we should rephrase something be or even would like to provide a new quiz question or coding example? Please consider providing a pull request. To do so, please check out the devel branch of the repo and add your fixes there. Writing an e-mail or opening an issue with suggested improvements is obviously very welcome as well!
  • You are none of the above but would like to contribute, get in touch / open issues / create pull requests! We are happy about any help.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

We would appreciate if you contact us in case you are re-using our course. Knowing this helps us to keep the project alive. Thank you!

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Data Science in a Box follows a similar philosophy as this course, with much more emphasis on learning R, doing data visualization and understanding more classical statistical inference methods (hypothesis tests, linear regression, etc.)