# 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

## Lecture slides

- Organization slides
- Optimization 1: Introduction and local methods slides
- Optimization 2: More advanced methods slides
- Introduction to machine learning slides
- Gaussian process slides
- Gaussian process (closer look) slides
- Bayesian optimization slides
- Modern neural networks slides
- Uncertainty quantification in machine learning slides
- Unsupervised learning slides
- Reinforcement learning slides
- Current Challenges in Machine Learning for Accelerators slides

## 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.