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Overview

This page gathers the class material for the winter 2022 U.S. Particle Accelerator School course on Optimization and Machine Learning for Accelerators.

Agenda

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Lecture slides

  • 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

Labs

The lab exercises are in Jupyter notebook format, and can be downloaded from the following Github repository: github.com/uspas/optimization_and_ml

During this course, the notebooks will be run on the Radiasoft Jupyter servers, at jupyter.radiasoft.org.

Slack

The course will use the Slack workspace uspas-ml-winter-2022.slack.com for related communication, discussions and questions.