# Nuclear Talent course on Machine Learning in Nuclear Experiment and Theory

Daniel Bazin [1]
Morten Hjorth-Jensen [1]
Michelle Kuchera [2]
Sean Liddick [3]
Raghuram Ramanujan [4]

[1] Department of Physics and Astronomy and Facility for Rare Ion Beams and National Superconducting Cyclotron Laboratory, Michigan State University, East Lansing, Michigan, USA
[2] Physics Department, Davidson College, Davidson, North Carolina, USA
[3] Department of Chemistry and Facility for Rare Ion Beams and National Superconducting Cyclotron Laboratory, Michigan State University, East Lansing, Michigan, USA
[4] Department of Mathematics and Computer Science, Davidson College, Davidson, North Carolina, USA

## Monday July 19: Introduction

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## Monday July 20: Linear Regression

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## Tuesday July 21: Logistic Regression and Gradient Methods

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## Wednesday July 22: Decision Trees, Random Forests, Bagging and Boosting

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## Thursday July 23: Introduction to Neural Networks and Deep Learning

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## Friday July 24: Beta-decay experiments, how to analyze various events, with hands-on examples

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## Monday July 26: Deep Learning and Neural networks

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## Tuesday July 27: From Neural Networks to Convolutional Neural Networks

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## Wednesday July 28: Discussion of nuclear experiments and how to analyze data, presentation of simulated data from Active-Target Time-Projection Chamber

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## Thursday July 29: Generative models

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## Friday July 30: Reinforcement Learning. Future directions in machine learning and summary of course.

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## Projects and Exercises

### Project on analysis of own data

Note that the remaining days we will present and discuss how to read and handle data from experiments. The videos should give you all relevant details as well as the notebooks.

General learning book on statistical analysis:

• Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer

General Machine Learning Books:

• Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
• Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer
• David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press
• David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press

Excellent Videos on TensorFlow and other Machine Learning topics

## Why a course on Machine Learning for Nuclear Physics?

Probability theory and statistical methods play a central role in science. Nowadays we are surrounded by huge amounts of data. For example, there are about one trillion web pages; more than one hour of video is uploaded to YouTube every second, amounting to 10 years of content every day; the genomes of 1000s of people, each of which has a length of more than a billion base pairs, have been sequenced by various labs and so on. This deluge of data calls for automated methods of data analysis, which is exactly what machine learning provides. The purpose of this Nuclear Talent course 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 and statistical data analysis, such as various regression methods before we move into deep learning methods for both supervised and unsupervised learning, with an emphasis on the analysis of nuclear physics experiments and theoretical nuclear physics. The students will work on hands-on daily examples as well as projects than can result in final credits. Exercises and projects will be provided and the aim is to give the participants an overview on how machine learning can be used to analyze and study nuclear physics problems (experiment and theory). The major scope is to give the participants a deeper understanding on what Machine learning and Data Analysis are and how they can be used to analyze data from nuclear physics experiments and perform theoretical calculations of nuclear many-body systems.

The goals of the Nuclear Talent course on Machine Learning and Data Analysis are to give the participants a deeper understanding and critical view of several widely popular Machine Learning algorithms, covering both supervised and unsupervised learning. The learning outcomes involve an understanding of the following central methods:

• Basic concepts of machine learning and data analysis and statistical concepts like expectation values, variance, covariance, correlation functions and errors;
• Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;
• Optimization of functions
• Linear Regression and Logistic Regression;
• Dimensionality reductions, from PCA to clustering
• Boltzmann machines;
• Neural networks;
• Decisions trees and Random Forests
• Support Vector Machines
• Convolutional Neural Networks and deep learning
• Recurrent Neural Networks and Autoenconders
and their applications to nuclear physics problems. We are targeting an audience of graduate students (both Master of Science and PhD) as well as post-doctoral researchers in nuclear experiment and theory.

The teaching teams consists of both theorists and experimentalists. We believe such a mix is important as it gives the students a better understanding on how data are obtained, and what are the limitations and possibilities in understanding and interpreting the experimental information.

## Introduction to the Talent Courses

A recently established initiative, Training in Advanced Low Energy Nuclear Theory, aims at providing an advanced and comprehensive training to graduate students and young researchers in low-energy nuclear theory. The initiative is a multinational network between several European and Northern American institutions and aims at developing a broad curriculum that will provide the platform for a cutting-edge theory for understanding nuclei and nuclear reactions. These objectives will be met by offering series of lectures, commissioned from experienced teachers in nuclear theory. The educational material generated under this program will be collected in the form of WEB-based courses, textbooks, and a variety of modern educational resources. No such all-encompassing material is available at present; its development will allow dispersed university groups to profit from the best expertise available.