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This tutorial explores Machine Learning using scikit-learn and PyTorch for applications in high energy physics.

Extended from a version developed by Luke Polson for the 2020 USATLAS Computing Bootcamp.

• 🎥 Fixme Sample event Dec 1 - 13, 2022

Emoji key: 🎥 (full video recordings availabile), ⛏️ (hackathon)

We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.

If you make non-trivial changes (i.e., more than fixing a simple typo), you are eligible to be added to the HSF Training Community page, as well as to the list of contributors below.

We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes.

Quick summary of how to get a local preview: Install jekyll and then run

bundle install
bundle update
bundle exec jekyll serve

Unless we change framework versions, only the last command needs to be typed after the first time.

Before committing anything, we also ask you to install the pre-commit hooks of this repository:

pip3 install pre-commit
pre-commit install

Please see the current list of issues for ideas for contributing to this repository. For making your contribution, we use the GitHub flow, which is nicely explained in the chapter Contributing to a Project in Pro Git by Scott Chacon. Look for the tag ![good_first_issue], which marks particularly simple issues to get you started.

• Meirin Oan Evans

Thanks goes to these wonderful people (emoji key) who contributed to the content of the lesson:

Even more people contributed to the framework, but they are too many to list! Instead, all regular contributors are listed on our HSF Training Community page.