21.09.2020 - 02.10.2020

Monday, 13:00 bis Friday, 16:30


Helmholtz Virtual ML Summer School 2020

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powered by Helmholtz Information & Data Science Academy (HIDA)
in cooperation with Helmholtz Artificial Intelligence Cooperation Unit (Helmholtz AI), Munich School for Data Science (MUDS), Ludwig-Maximilians-Universität München (LMU) and Munich Center for Machine Learning (MCML)



Core program of the Virtual ML Summer School 2020 is an introductory course to fundamental techniques and concepts of supervised Machine Learning, which has become a central part of modern data analysis.
In particular non-linear and non-parametric methods have been used successfully in uncovering complex patterns and relationships by computer scientists and statisticians.



Sep 21
Welcome and opening by Fabian Theis
ML Basics
Supervised regression

Sep 22
Supervised regression

Sep 23
HIDA virtual career day

Sep 24
Supervised Classification

Sep 25
Keynote by Bernd Bischl on mlr3
Supervised Classification


Sep 28

Sep 29
Helmholtz AI showcase von Dominik Thalmeier: "Using anomaly detection to identify mutations that effect hearing behaviour"
Random Forests

Sep 30
Random Forests

Oct 1
Helmholtz AI showcase von Christian L. Müller:"Sparse predictive modeling of microbiome data"

Oct 2
Practical Advice

The focus of the course is to give a basic understanding of the different algorithms, models and concepts while explaining the necessary mathematical foundation.

Participants will acquire theoretical as well as practical competencies regarding some fundamental models of learning from data. Also participants will be enabled to conduct a data analysis project, including understanding and interpreting the data, in order to critically judge the advantages and disadvantages of the different methods.

Virtual meetings and group work (except Sep 23) take place from 1-4:30pm.

You are required to work through course materials (videos, quizzes, online exercises) in prepartion for the live sessions by yourself and at your own pace. The live sessions will be used to put the concepts you learned about into practice.

You will have access to the course materials a couple of weeks prior to the starting date. It is up to you if you prepare topics in the mornings before each session in the afternoon or in the weeks before the course starts.



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



Bernd Bischl (LMU München, MCML)
Tobias Pielok (LMU München)
Heidi Seibold (Helmholtz AI)


Show Cases

Christian L. Müller (Helmholtz AI, HMGU München, LMU München, Flatiron Institute New York)
Dominik Thalmeier (Helmholtz AI)


Course Compendia

The program of the Helmholtz Virtual ML Summer School builds on the course program Introduction to Machine Learning (I2ML), which was developed by Bernd Bischl, Fabian Scheipl, Heidi Seibold, Christoph Molnar and Daniel Schalk. Concept and materials are accessible and licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). If you use the course, the initiators are looking forward to your feedback!



The deadline for registration was July 31 2020.

Registrations that we received afterwards were automatically put on the waiting list. The demand was very high, so we will try to offer more events of this kind in the future.

Please send us an email to if you would like to participate in similar events and let us know which Information & Data Science topics you would like to learn more about!

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MUDS, Helmholtz AI and HIDA are part of the Helmholtz Incubator Information & Data Science.