Introduction to Machine Learning for Accelerator Physics
This is an incremental educational resource for accelerator physics students to learn ML basics.
The paper introduces machine learning concepts and terminology to accelerator physics students, covering frameworks from linear regression to neural networks and applying them to examples including free-electron lasers.
This pair of CAS lectures gives an introduction for accelerator physics students to the framework and terminology of machine learning (ML). We start by introducing the language of ML through a simple example of linear regression, including a probabilistic perspective to introduce the concepts of maximum likelihood estimation (MLE) and maximum a priori (MAP) estimation. We then apply the concepts to examples of neural networks and logistic regression. Next we introduce non-parametric models and the kernel method and give a brief introduction to two other machine learning paradigms, unsupervised and reinforcement learning. Finally we close with example applications of ML at a free-electron laser.