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))

19/07/2021, 14:00

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

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

20/07/2021, 14:00

3. Decision Trees, Random Forests and Boosting methods

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

21/07/2021, 14:00

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

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

22/07/2021, 14:00

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

Prof. Sean Liddick (Michigan State University)

23/07/2021, 14:00

6. Neural Networks and Deep Learning

Prof. Raghuram Ramanujan (Davidson College)

26/07/2021, 14:00

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

Prof. Michelle Kuchera (Davidson College)

27/07/2021, 14:00

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)

28/07/2021, 14:00

9. Generative models

Prof. Michelle Kuchera (Davidson College)

29/07/2021, 14:00

10. Reinforcement Learning

Prof. Raghuram Ramanujan (Davidson College)

30/07/2021, 14:00

11. Exercises