Content, literature and requirements

Content of the lecture

• Introduction to machine learning - general concepts, terms, challenges and problems
• Regression, Naive-Bayes, Support Vector Machines, Principal Component analysis, Clustering, ...
• Neural networks and deep learning
• Reinforcement learning
• Applying machine learning with Python to examples from different areas of science

Literature

The lecture is mainly based on the following sources (don't panic, even if the word physics appears in the first reference)

• A. Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems
• M. Nielsen, Neural Networks and Deep Learning, http: //neuralnetworksanddeeplearning.com
• Christopher M. Bishop, Pattern Recognition and Machine Learning
• R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction

• A. Burkov, The Hundred-Page Machine Learning Book
• P. Mehta et. al., A high-bias, low-variance introduction to Machine Learning for physicists, arXiv: 1803.08823 (2019)

The lecture gives a more comprehensive overview of the literaur.

Requirements

To understand the algorithms

• A little bit of linear algebra
• Derivatives in one and more dimensions

To implement the methods

• Elementary programming skills in Python

The following libraries are used in the implementation (available for all operating systems - will be updated)

• For implementation we use Scikit-learn, Tensorflow (Keras) or Pytorch and for advanced reinforcement learning gym

• Jupyter Notebook is helpful which is also available online from the WWU via jupyterhub

Alternatively (everything online, i.e. nothing needs to be installed + large computer resources)

Kaggle