This page gathers the class material for the winter 2022 U.S. Particle Accelerator School course on Optimization and Machine Learning for Accelerators.
- Organization slides
- Optimization 1: Introduction and local methods slides
- Optimization 2: More advanced methods slides
- Introduction to machine learning slides
- Gaussian processes
- Bayesian optimization
- Modern neural networks
- Uncertainty quantification in machine learning
- Unsupervised learning
- Reinforcement learning
- Current Challenges in Machine Learning for Accelerators
The lab exercises are in Jupyter notebook format, and can be downloaded from the following Github repository: github.com/uspas/optimization_and_ml
The course will use the Slack workspace uspas-ml-winter-2022.slack.com for related communication, discussions and questions.