Linear Regression and intro to statistical data analysis

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

Logistic Regression and classification problems, intro to gradient methods

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

Decision Trees, Random Forests and Boosting methods

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

Basics of Neural Networks and writing your own Neural Network code

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

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

• Sean Liddick (Michigan State University)

Neural Networks and Deep Learning

• Raghuram Ramanujan (Davidson College)

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

• Michelle Kuchera (Davidson College)

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

• Daniel Bazin (Michigan State University)

Generative models

• Michelle Kuchera (Davidson College)

Reinforcement Learning

• Raghuram Ramanujan (Davidson College)