Abstract We first introduce various algorithms and techniques for machine learning and data science. While there is a strong focus on neural network applications in unsupervised, supervised and reinforcement learning, other machine learning techniques are discussed as well. These include various clustering and anomaly detection algorithms, support vector machines, and decision trees. In addition, we review data science techniques such as genetic algorithms and topological data analysis. This first part of the review makes some reference to concepts in physics, but the explanations and examples do not assume any knowledge of string theory and should therefore be accessible to a wide variety of readers with a physics background. After that, we illustrate applications to string theory. We give an overview of existing string theory data sets and describe how they can be studied using data science techniques. We also explain the computational complexity involved in the investigation of string vacua. Example codes that illustrate the techniques introduced in this review are available from Fabian Ruehle (0000).

中文翻译：

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数据科学在弦论中的应用

摘要 我们首先介绍机器学习和数据科学的各种算法和技术。虽然非常关注无监督学习、监督学习和强化学习中的神经网络应用，但也讨论了其他机器学习技术。其中包括各种聚类和异常检测算法、支持向量机和决策树。此外，我们还回顾了遗传算法和拓扑数据分析等数据科学技术。评论的第一部分参考了物理学中的一些概念，但解释和示例不假设任何弦理论知识，因此应该可供具有物理学背景的各种读者使用。之后，我们将说明在弦论中的应用。我们概述了现有的弦论数据集，并描述了如何使用数据科学技术研究它们。我们还解释了字符串真空研究中涉及的计算复杂性。说明本评论中介绍的技术的示例代码可从 Fabian Ruehle (0000) 获得。