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A list of all pages that have property "Abstract" with value "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 Jupyter 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.". Since there have been only a few results, also nearby values are displayed.

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    • A high-bias low-variance introduction to machine learning for physicists  + (Machine Learning (ML) is one of the most eMachine Learning (ML) is one of the most exciting and dynamic areas of modern research</br>and application. The purpose of this review is to provide an introduction to the core</br>concepts and tools of machine learning in a manner easily understood and intuitive</br>to physicists. The review begins by covering fundamental concepts in ML and modern</br>statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization</br>before moving on to more advanced topics in both supervised and unsupervised learning.</br>Topics covered in the review include ensemble models, deep learning and neural networks,</br>clustering and data visualization, energy-based models (including MaxEnt models and</br>Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize</br>the many natural connections between ML and statistical physics. A notable aspect of</br>the review is the use of Jupyter notebooks to introduce modern ML/statistical packages</br>to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations</br>of supersymmetric decays of proton-proton collisions). We conclude with an extended</br>outlook discussing possible uses of machine learning for furthering our understanding</br>of the physical world as well as open problems in ML where physicists maybe able to</br>contribute.where physicists maybe able to contribute.)