機器學習（ML）是對計算機系統(tǒng)使用的算法和統(tǒng)計模型的科學研究，這些算法和統(tǒng)計模型不使用顯式指令，而是依靠模式和推理來有效地執(zhí)行特定的任務。它被視為人工智能的一個子集。機器學習算法建立一個樣本數(shù)據(jù)的數(shù)學模型，稱為“訓練數(shù)據(jù)”，以便在沒有明確編程來執(zhí)行任務的情況下做出預測或決策。機器學習算法被廣泛應用于各種各樣的應用中，如電子郵件過濾和計算機視覺，在這些應用中，它對數(shù)據(jù)是不可行的。執(zhí)行任務的特定指令的算法。機器學習與計算統(tǒng)計密切相關，計算統(tǒng)計集中于使用計算機進行預測。數(shù)學優(yōu)化的研究為機器學習領域提供了方法、理論和應用領域。數(shù)據(jù)挖掘是機器學習中的一個研究領域，其重點是通過無監(jiān)督學習進行探索性數(shù)據(jù)分析在其跨業(yè)務問題的應用中，機器學習也稱為預測分析。

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.