Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.[3][4] In its application across business problems, machine learning is also referred to as predictive analytics.
机器学习（ML）是对计算机系统使用的算法和统计模型的科学研究，这些算法和统计模型不使用显式指令，而是依靠模式和推理来有效地执行特定的任务。它被视为人工智能的一个子集。机器学习算法建立一个样本数据的数学模型，称为“训练数据”，以便在没有明确编程来执行任务的情况下做出预测或决策。机器学习算法被广泛应用于各种各样的应用中，如电子邮件过滤和计算机视觉，在这些应用中，它对数据是不可行的。执行任务的特定指令的算法。机器学习与计算统计密切相关，计算统计集中于使用计算机进行预测。数学优化的研究为机器学习领域提供了方法、理论和应用领域。数据挖掘是机器学习中的一个研究领域，其重点是通过无监督学习进行探索性数据分析在其跨业务问题的应用中，机器学习也称为预测分析。
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