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Machine Learning applied to Nuclear Physics, experiment and theory
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.
|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)|