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- A high-bias low-variance introduction to machine learning for physicists + (Machine Learning (ML) is one of the most e … Machine 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.)