Machine Learning for Nuclear Physics and the Electron Ion Collider (HUGS2023)#

This website hosts a mini-series of lectures on AI/ML for Nuclear Physics and the Electron Ion Collider, taught at HUGS2023.

This is the introduction page. You can navigate the lectures contained in this book.

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Synopsis: The HUGS2023 lectures on Machine Learning for Nuclear Physics and the Electron Ion Collider are a focused 6-hour program designed to introduce physics graduate students to the fundamentals of artificial intelligence and machine learning (AI/ML), and their applications in nuclear physics (NP), specifically concerning the Electron Ion Collider (EIC) project.

The course aims to equip students with a basic understanding of AI/ML basics, and how these techniques can be utilized to interpret and analyze NP data. A key component of these lectures is exploring the role of AI/ML in making sense of the datasets anticipated from the EIC project.

The students will gain a practical understanding of AI/ML concepts through hands-on exercises using simulated NP data. The lectures serve as a launchpad for those aspiring to integrate AI/ML techniques into their NP research while also offering valuable insights into some of the latest AI/ML initiatives ongoing at both the JLab and EIC.

This course is based on the following references [1, 2, 3, 4, 5, 6]

Datasets / Modules