# Overview

This page gathers the class material for the 2021 U.S. Particle Accelerator School course on Optimization and Machine Learning for Accelerators.

## Agenda

## Lecture slides and recordings

- Organization: slides / recording
- Optimization 1: Introduction and local methods: slides / recording
- Optimization 2: More advanced methods: slides / recording
- Introduction to machine learning: slides
- Gaussian processes: slides
- Bayesian optimization: slides
- Modern neural networks: slides
- Uncertainty quantification in machine learning: slides / recording
- Unsupervised learning: slides / recording
- 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/2021_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.slack.com for related communication, discussions and questions.