A highbias, lowvariance introduction to Machine Learning for physicists
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 biasvariance tradeoff, overfitting, regularization, generalization, and gradient descent 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, energybased 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 Jupyter notebooks to introduce modern ML/statistical packages to readers using physicsinspired datasets (the Ising Model and MonteCarlo simulations of supersymmetric decays of protonproton 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 may be able to contribute.
 Publication:

Physics Reports
 Pub Date:
 May 2019
 DOI:
 10.1016/j.physrep.2019.03.001
 arXiv:
 arXiv:1803.08823
 Bibcode:
 2019PhR...810....1M
 Keywords:

 Physics  Computational Physics;
 Condensed Matter  Statistical Mechanics;
 Computer Science  Machine Learning;
 Statistics  Machine Learning
 EPrint:
 Notebooks have been updated. 122 pages, 78 figures, 20 Python notebooks