FRIB TA Summer School - Machine Learning Applied to Nuclear Physics

Facility for Rare Isotope Beams

Facility for Rare Isotope Beams

640 South Shaw Lane, East Lansing, MI 48824
Matthew Hirn (CSME and Math MSU), Michelle Kuchera (Davidson College), Morten Hjorth-Jensen (FRIB/NSCL and Physics & Astronomy MSU)

The FRIB TA Summer School - Machine Learning Applied to Nuclear Physics will take place at the Facility for Rare Isotope Beams (FRIB) on the Michigan State University campus in East Lansing, MI from May 20 to 23, 2019, in room 1200 Laboratory.

Machine Learning (ML) 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.

We plan hands-on sessions in the afternoons where relevant software is presented, allowing thereby the participants to get started with machine learning algorithms. We will emphasize widely used Python packages like scikit-learn, Tensorflow and Keras.

The school is organized by the FRIB Theory Alliance. For selected participants we hope to provide partial support which may include lodging and meals. We will not provide support for transportation expenses.

FRIB-TA Local Organizing Committee
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