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, 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, 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 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 may be able to contribute.

中文翻译：

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为物理学家提供的高偏差、低方差的机器学习介绍


机器学习（ML）是现代研究和应用中最令人兴奋和最具活力的领域之一。本次综述的目的是以物理学家易于理解和直观的方式介绍机器学习的核心概念和工具。本综述首先介绍机器学习和现代统计学中的基本概念，例如偏差-方差权衡、过度拟合、正则化、泛化和梯度下降，然后再讨论监督和无监督学习中更高级的主题。评论涵盖的主题包括集成模型、深度学习和神经网络、聚类和数据可视化、基于能量的模型（包括 MaxEnt 模型和受限玻尔兹曼机）以及变分方法。自始至终，我们都强调机器学习和统计物理学之间的许多自然联系。该评论的一个值得注意的方面是使用 Python Jupyter 笔记本向使用物理学启发的数据集（质子-质子碰撞超对称衰变的伊辛模型和蒙特卡罗模拟）的读者介绍现代机器学习/统计包。最后，我们以扩展的前景讨论了机器学习的可能用途，以加深我们对物理世界的理解，以及物理学家可以做出贡献的机器学习中的开放问题。