Machine Learning applied to Nuclear Physics, Experiment and Theory

Contribution List

1. Linear Regression and intro to statistical data analysis

Morten Hjorth-Jensen (Michigan State University (USA))

7/19/21, 2:00 PM

2. Logistic Regression and classification problems, intro to gradient methods

Morten Hjorth-Jensen (Michigan State University (USA))

7/20/21, 2:00 PM

3. Decision Trees, Random Forests and Boosting methods

Morten Hjorth-Jensen (Michigan State University (USA))

7/21/21, 2:00 PM

4. Basics of Neural Networks and writing your own Neural Network code

Morten Hjorth-Jensen (Michigan State University (USA))

7/22/21, 2:00 PM

5. Beta-decay experiments, how to analyze various events, with hands-on examples

Prof. Sean Liddick (Michigan State University)

7/23/21, 2:00 PM

6. Neural Networks and Deep Learning

Prof. Raghuram Ramanujan (Davidson College)

7/26/21, 2:00 PM

7. From Neural Networks to Convolutional Neural Networks and how to analyze experiment (classification of events and real data)

Prof. Michelle Kuchera (Davidson College)

7/27/21, 2:00 PM

8. Discussion of nuclear experiments and how to analyze data, presentation of simulated data from Active-Target Time-Projection Chamber (AT-TPC)

Prof. Daniel Bazin (Michigan State University)

7/28/21, 2:00 PM

9. Generative models

Prof. Michelle Kuchera (Davidson College)

7/29/21, 2:00 PM

10. Reinforcement Learning

Prof. Raghuram Ramanujan (Davidson College)

7/30/21, 2:00 PM

11. Exercises