OT-SC-WS-03 | Machine learning

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Prof. Dr. Marvin N. Wright





Nowadays, machine learning is everywhere: old and new questions, problems and challenges are tackled with machine learning, sometimes with great success, sometimes not. To successfully use machine learning and understand its limitations, we have to go beyond buzzword bingo and learn the general and basic concepts of machine learning.


This workshop teaches the major concepts of machine learning. We focus on general principles instead of going into full depth of single methods. We will learn the difference between supervised and unsupervised learning, important notation such as models and learners, why training errors are different than test errors and how to optimize and evaluate prediction performance. Nevertheless, the course covers the most important machine learning methods such as k-nearest neighbors, decision trees, random forests, boosting, support vector machines and artificial neural networks. The methods will be introduced in a non-technical and intuitive way. These theory sessions will be complemented by hands-on sessions in R, where the methods are applied in practice.


Understand basic concepts of machine learning:

  • Supervised and unsupervised learning
  • Difference between models and learners, training and test errors, etc.
  • Over- and underfitting
  • Hyperparameter tuning
  • Performance evaluatio

Know the major machine learning methods:

  • K-Nearest Neighbors
  • Decision Trees
  • Random Forests
  • Boosting
  • Support Vector Machines
  • Artificial Neural Networks

Be able to perform machine learning analyses in R:

  • Model fitting
  • Hyperparameter tuning
  • Performance evaluation
  • Benchmarking
  • Visualizing results


Prior knowledge

Advances math is generally NOT required (only for short part on support vector machines); Basic programming skills are required.



  • Own PC, laptop
  • Internet, browser (up-to-date)
  • For online format a second screen might be beneficial

General intro:


Both books are freely available on the websites.



9:00-12:00 | break | 13:00-16:00 (on each day)


Online via VC



Marvin N. Wright

Prof. Dr. Marvin N. Wright

  • Professor of Machine Learning in Statistics
  • Emmy Noether Junior Research Group Leader at the Leibniz Institute for Epidemiology and Prevention Research