[Cuga] Summer School Announcement: Machine Learning Applied to Nuclear Physics

[Jodi Patient]

FRIB User Contact [fribusercontact at fribusers.org]
[https://mail.vuu.edu/owa/14.3.399.0/themes/resources/clear1x1.gif][https://mail.vuu.edu/owa/14.3.399.0/themes/resources/clear1x1.gif][https://mail.vuu.edu/owa/14.3.399.0/themes/resources/clear1x1.gif]
Announcing a FRIB-Theory Alliance Summer School: “Machine Learning Applied to Nuclear Physics”

Machine Learning is one of the most exciting and dynamic areas of modern research and application. The purpose of this summer school 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, with an emphasis on the analysis of nuclear physics experiments and theoretical nuclear physics.
Hands-on examples will be provided and the aim is to give the participants an overview on how machine learning can be used to analyze and study nuclear physics problems.

Lecturers:
Morten Hjorth-Jensen (FRIB/NSCL and Physics & Astronomy MSU) - Linear and logistic regression, decision trees and random forests, neural networks and Boltzmann machines
Matthew Hirn (CSME and Math MSU) - Unsupervised learning and quantum mechanical problems
Michelle Kuchera (Davidson College) - Convolutional Neural Networks, Recurrent Neural Networks and analysis of nuclear physics experiments

Date and location:
May 20-23, 2019 at the FRIB, East Lansing, MI

For more information and to apply for the summer school visit the website<https://gcc01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmail.vuu.edu%2Fowa%2Fredir.aspx%3FC%3DQ65Wr7EUb_WQV0GcoOkRkgsSFr0V4eaXXLzaesp4DGpbiljDTL_WCA..%26URL%3Dhttps%253a%252f%252findico.frib.msu.edu%252fevent%252f16%252f&data=02%7C01%7C%7C75e6589ddb59499abe0808d6bf7df4c5%7Cb4d7ee1f4fb34f0690372b5b522042ab%7C1%7C0%7C636906945022483461&sdata=j5clIf3miMsYkQlJBw4bKyRM3%2BlTSST%2BWjjsQXd1dPs%3D&reserved=0>.

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