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Machine Learning applied to Nuclear Physics, experiment and theory


Barbara Gazzoli
+39 0461 314763
Monday, 22 June, 2020 - 14:00 to Friday, 3 July, 2020 - 18:00
ONLINE | Special edition

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this Nuclear Talent course is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists and nuclear physicists in particular. We will start with some of the basic methods from supervised learning, such as various regression methods before we move into deep learning methods for both supervised and unsupervised learning.

Registration period: 
31 Dec 2019 to 31 May 2020


Daniel Bazin Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory Michigan USA
Morten Hjorth-Jensen Michigan State University (USA) and University of Oslo (Norway)
Michelle Kuchera Davidson College (USA)
Sean Liddick Michigan State University (USA)
Raghuram Ramanujan Davidson College (USA)