Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an
introduction to the core concepts and tools of machine learning in a manner
easily understood and intuitive to physicists. The review begins by covering
fundamental concepts in ML and modern statistics such as the bias-variance
tradeoff, overfitting, regularization, and generalization before moving on to
more advanced topics in both supervised and unsupervised learning. Topics
covered in the review include ensemble models, deep learning and neural
networks, clustering and data visualization, energy-based models (including
MaxEnt models and Restricted Boltzmann Machines), and variational methods.
Throughout, we emphasize the many natural connections between ML and
statistical physics. A notable aspect of the review is the use of Python
notebooks to introduce modern ML/statistical packages to readers using
physics-inspired datasets (the Ising Model and Monte-Carlo simulations of
supersymmetric decays of proton-proton collisions). We conclude with an
extended outlook discussing possible uses of machine learning for furthering
our understanding of the physical world as well as open problems in ML where
physicists maybe able to contribute. (Notebooks are available at
https://physics.bu.edu/~pankajm/MLnotebooks.html )
%0 Generic
%1 mehta2018highbias
%A Mehta, Pankaj
%A Bukov, Marin
%A Wang, Ching-Hao
%A Day, Alexandre G. R.
%A Richardson, Clint
%A Fisher, Charles K.
%A Schwab, David J.
%D 2018
%K machine_learning
%T A high-bias, low-variance introduction to Machine Learning for
physicists
%U http://arxiv.org/abs/1803.08823
%X Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an
introduction to the core concepts and tools of machine learning in a manner
easily understood and intuitive to physicists. The review begins by covering
fundamental concepts in ML and modern statistics such as the bias-variance
tradeoff, overfitting, regularization, and generalization before moving on to
more advanced topics in both supervised and unsupervised learning. Topics
covered in the review include ensemble models, deep learning and neural
networks, clustering and data visualization, energy-based models (including
MaxEnt models and Restricted Boltzmann Machines), and variational methods.
Throughout, we emphasize the many natural connections between ML and
statistical physics. A notable aspect of the review is the use of Python
notebooks to introduce modern ML/statistical packages to readers using
physics-inspired datasets (the Ising Model and Monte-Carlo simulations of
supersymmetric decays of proton-proton collisions). We conclude with an
extended outlook discussing possible uses of machine learning for furthering
our understanding of the physical world as well as open problems in ML where
physicists maybe able to contribute. (Notebooks are available at
https://physics.bu.edu/~pankajm/MLnotebooks.html )
@misc{mehta2018highbias,
abstract = {Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an
introduction to the core concepts and tools of machine learning in a manner
easily understood and intuitive to physicists. The review begins by covering
fundamental concepts in ML and modern statistics such as the bias-variance
tradeoff, overfitting, regularization, and generalization before moving on to
more advanced topics in both supervised and unsupervised learning. Topics
covered in the review include ensemble models, deep learning and neural
networks, clustering and data visualization, energy-based models (including
MaxEnt models and Restricted Boltzmann Machines), and variational methods.
Throughout, we emphasize the many natural connections between ML and
statistical physics. A notable aspect of the review is the use of Python
notebooks to introduce modern ML/statistical packages to readers using
physics-inspired datasets (the Ising Model and Monte-Carlo simulations of
supersymmetric decays of proton-proton collisions). We conclude with an
extended outlook discussing possible uses of machine learning for furthering
our understanding of the physical world as well as open problems in ML where
physicists maybe able to contribute. (Notebooks are available at
https://physics.bu.edu/~pankajm/MLnotebooks.html )},
added-at = {2018-10-14T11:14:39.000+0200},
author = {Mehta, Pankaj and Bukov, Marin and Wang, Ching-Hao and Day, Alexandre G. R. and Richardson, Clint and Fisher, Charles K. and Schwab, David J.},
biburl = {https://www.bibsonomy.org/bibtex/2f8740a2d2e6a0936c411ebbcedb24b36/supremefacist},
interhash = {3a510bccd5545871323d5e1a1ba06af2},
intrahash = {f8740a2d2e6a0936c411ebbcedb24b36},
keywords = {machine_learning},
note = {cite arxiv:1803.08823Comment: 119 pages, 78 figures, 20 Python notebooks. Comments are welcome!},
timestamp = {2018-10-14T11:14:39.000+0200},
title = {A high-bias, low-variance introduction to Machine Learning for
physicists},
url = {http://arxiv.org/abs/1803.08823},
year = 2018
}