Summer School:

HIDA Virtual ML Summer School 2021

Tuesday, 20.07. - Monday, 06.09.2021 · 9:30-13:00
online - 2 Blocks

Immersion in machine learning

The response to the Virtual ML Summer School 2020 was tremendous. We are therefore particularly pleased to offer this program again in 2021. At its core is an introductory course in basic techniques and concepts of supervised machine learning, which has become a central part of modern data analysis.

powered by HIDA, in cooperation with Helmholtz AI, MUDS, LMU, MCML

## This course is taught in English ##

Dive into Machine Learning

The course is designed to be as self-contained as possible to cover most relevant areas of supervised Machine Learning (ML). The introductory parts aim more at a practical and operational understanding of the algorithms and models covered. In the more advanced sections, we also include sound theoretical foundations and proofs in order to convey ML theory in a self-contained and as precise way as possible.

The approach follows the flipped classroom method: participants are expected to work through the course materials (videos, quizzes, online exercises) independently and at their own pace to prepare for the live sessions. Live online sessions and group work will be used to put the concepts learned into practice.

The live sessions will take place in two consecutive blocks. Participants register for both blocks.

Block Program:

Block 1 July 21-27, 2021 (excluding weekends), 9:30 a.m. to 1 p.m. CEST.

  •     21.07. ML Basics
  •     22.07. Regression
  •     23.07. Classification
  •     26.07. Classification knn
  •     27.07. Evaluation

Block 2 September 1 - 7, 2021 (weekend excluded), 9:30 a.m. - 1 p.m. CEST

  •     01.09. Evaluation
  •     02.09. Trees
  •     03.09. Random forests
  •     06.09. Voting
  •     07.09. Practical hints

If you want to learn more about the course content, please have a look at the course overview.

In addition to the course program, Helmholtz AI Consultants will provide insights into current research projects and the application of ML methods, time & date tba.


In advance of the course, you will have access to the course materials and organize your own study time: You can prepare individual topics the day before the live sessions or in the weeks before the course starts.

The course is taught in English and is designed for ML beginners with a basic, university education in mathematics and statistics:

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

References for prerequisites


  •     Bernd Bischl (LMU Munich, MCML)
  •     Ludwig Bothmann (LMU Munich)
  •     Tobias Pielok (LMU Munich)

Register here

The program builds on the Introduction to Machine Learning (I2ML) course developed by Bernd Bischl, Fabian Scheipl, Heidi Seibold, Ludwig Bothmann, Christoph Molnar, Daniel Schalk, and Tobias Pielok. Concept and materials are freely available under Creative Commons Attribution 4.0 International (CC BY 4.0). If used, the initiators would appreciate a note!

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