US ATLAS Machine Learning Training 2023

Aishik Ghosh, Elham E Khoda & Ben Nachman

Local Organizing team

July 28, 2023

Summary

• ~100 registrations (capped) and few unregistered folks joined on zoom, we could supported all registered participants with a NERSC account for tutorials

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• Goal was to cover breath of physics uses, ML algorithms, good practices and leave links for you to deep dive into topics of interest

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• New additions: Generative model, differentiable programming tutorials�
• Materials should act as a resource you can refer back to as you need�

4 days of Learning

You’ve found people to chat with about ML

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Please continue to discuss with peers, ML experts you’ve met here

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Lasting connections with topic experts, who can help you bring ML back to your universities

A list we don’t want to be on…

https://reproducible.cs.princeton.edu

Resources

The tutorials github and slides will remain accessible

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Living review of ML for HEP: https://iml-wg.github.io/HEPML-LivingReview/

Initial resource page (not up-to-date):�https://github.com/iml-wg/HEP-ML-Resources

Discuss your questions with ATLAS-ML: https://atlas-talk.web.cern.ch/c/ml/

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We need your feedback !

We improved the program based on last year’s feedback. Please tell us what you liked and what could be improved !

Also helps to fund future events

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https://forms.gle/gbLsgNZt1SyXAidn9

A word of gratitude

ATC funding and all the support from Joe, Viviana, Mike

Vinicius, Sascha, Kehang, Radha for helping not just during the days of the program but also building up to it

Steve, Peter, Shashank, Rollin, Wahid and NERSC for supporting the training program, GPU nodes, live technical support and room booking:

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Big thanks to the admin Bianca, Adam, Liz, Viviana for helping make this happen

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Hope you enjoyed the program & Berkeley !