Tutorials

In recent years, we have given lectures and tutorials on machine topics at various occasions. I have gathered the available ones and provided the link below:

• Neural network basics
• Bayesian optimization
• Reinforcement Learning

Neural network basics

1. Tutorial on basic concepts of neural networks using PyTorch, with an example of training a small NN to classify the MNIST dataset. GitHub Repository

Bayesian optimization

1. Tutorial on basic Bayesian optimization techniques with an example for particle accelerator tuning using BoTorch. GitHub Repository
2. The same content was given as a guest lecture in the M.Sc. lecture series Numerical Methods of Accelerator Physics at TU Darmstadt. 2022/23 - Lecture 14 and 2023/24 - Lecture 11
3. Tutorial on basic Bayesian optimization using scikit-learn, originally given at the 10th MT-ARD-ST3 pre-meeting machine learning workshop (indico link). Link to the tutorial GitHub repository

Reinforcement Learning

1. Demonstration of using reinforcement learning to control the linear accelerator ARES, originally given at the RL4AA'23 workshop (indico link). The tutorial is further presented at the 4th ICFA Beam Dynamics ML-Workshop (indico link) and the 12th MT-ARD-ST3 pre-meeting machine learning workshop (indico link).
   • Link to the tutorial GitHub repository
2. Using Meta-RL and GP-MPC to tune the orbit of the AWAKE accelerator, originally given at the RL4AA'24 workshop (indico link).
   • Link to the tutorial GitHub repository