iml-wg/HEP-ML-Resources
Listing of useful learning resources for machine learning applications in high energy physics (HEPML)
This project helps high-energy physicists navigate the rapidly growing field of machine learning applications in their domain. It provides a curated list of educational materials like lectures, seminars, tutorials, and schools, along with links to relevant software, datasets, and research papers. Anyone involved in high energy physics research or education who wants to learn about or apply machine learning will find this resource valuable.
354 stars. No commits in the last 6 months.
Use this if you are a high-energy physicist looking for learning resources, tools, or research related to applying machine learning in your field.
Not ideal if you are looking for an exhaustive, real-time updated database of all machine learning research, or resources outside of high energy physics.
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354
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117
Language
TeX
License
MIT
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Last pushed
May 05, 2021
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