University of Heidelberg

Machine learning for physicists: an introduction

Canceld due to the corona pandemic

Overview

Machine learning is extremely successful in areas like image recognition, Speech processing and medical diagnosis. Methods are also used in physics machine learning is becoming increasingly important. This course gives a practical introduction to machine learning.
  • lecturer: Klaus Reygers
  • language of the course: deutsch
  • Place: INF 226, CIP 1.305
  • Date: 14.4. - 17.4.2020, 9:00-12:00 Uhr and 14:00-17:00 Uhr

Programming language

We'll use python 3 in the course. Basic knowdegle of the language is useful for this course. We'll work a lot with jupyter notebooks . A nice summary of important python commands is available on the website of the Stanford lecture CS231n.

Contents

Covered topics include:

  • Overview: Machine Learning
  • Fitting models to data
  • Tools: scikit-learn and KERAS
  • Linear models
  • Logistic regression
  • Boosted Decision Trees
  • Neural networks

    • References

  • Mehta et al., A high-bias, low-variance introduction to Machine Learning for physicists
  • Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning
  • K. Reygers, Introduction: Multivariate Analysis and Machine Learning
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