FRIB-TA Summer School on Machine Learning in Nuclear Experiment and Theory

Matthew Hirn [1]
Morten Hjorth-Jensen [2]
Michelle Kuchera [3]
Raghuram Ramanujan [4]

[1] Department of Mathematics and Department of Computational Science, Mathematics and Engineering, Michigan State University, East Lansing, Michigan, USA
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University, East Lansing, Michigan, USA
[3] Physics Department, Davidson College, Davidson, North Carolina, USA
[4] Department of Mathematics and Computer Science, Davidson College, Davidson, North Carolina, USA

Introduction to Data Analysis and Machine Learning

Regression Methods

Logistic Regression

Gradient Methods and Optimization

Decision trees, from simple to random ones

Support Vector Machines

Neural Networks

Convolutional Neural Networks

Unsupervised Learning, Boltzmann Machines

Bayesian Statistics and Bayesian Neural Networks

Recurrent Neural Networks

Autoencoders

Reinforcement Learning

Python and Scikit Learn, a short guide

Practicalities

  1. Five lectures per day Monday through Wednesday, starting at 830am, see schedule below
  2. Hands-on sessions in the afternoons till 6pm
  3. The last day, Thursday, is dedicated to solving explicit problems with hands-on guidance and relevant examples.

Recommended textbook

General learning book on statistical analysis:

General Machine Learning Books:

Schedule

Lectures are 50 min and there is a small break of 10 min between each lecture. Longer breaks at 1030am-11am and 3pm-330pm. Acronyms for teachers

Monday May 20, 2019: (breaks of 10 min for every lecture) Tuesday May 21, 2019: Wednesday May 22, 2019: Thursday May 23, 2019: