機(jī)器學(xué)習(xí)（ML）是對(duì)計(jì)算機(jī)系統(tǒng)使用的算法和統(tǒng)計(jì)模型的科學(xué)研究，這些算法和統(tǒng)計(jì)模型不使用顯式指令，而是依靠模式和推理來(lái)有效地執(zhí)行特定的任務(wù)。它被視為人工智能的一個(gè)子集。機(jī)器學(xué)習(xí)算法建立一個(gè)樣本數(shù)據(jù)的數(shù)學(xué)模型，稱為“訓(xùn)練數(shù)據(jù)”，以便在沒(méi)有明確編程來(lái)執(zhí)行任務(wù)的情況下做出預(yù)測(cè)或決策。機(jī)器學(xué)習(xí)算法被廣泛應(yīng)用于各種各樣的應(yīng)用中，如電子郵件過(guò)濾和計(jì)算機(jī)視覺(jué)，在這些應(yīng)用中，它對(duì)數(shù)據(jù)是不可行的。執(zhí)行任務(wù)的特定指令的算法。機(jī)器學(xué)習(xí)與計(jì)算統(tǒng)計(jì)密切相關(guān)，計(jì)算統(tǒng)計(jì)集中于使用計(jì)算機(jī)進(jìn)行預(yù)測(cè)。數(shù)學(xué)優(yōu)化的研究為機(jī)器學(xué)習(xí)領(lǐng)域提供了方法、理論和應(yīng)用領(lǐng)域。數(shù)據(jù)挖掘是機(jī)器學(xué)習(xí)中的一個(gè)研究領(lǐng)域，其重點(diǎn)是通過(guò)無(wú)監(jiān)督學(xué)習(xí)進(jìn)行探索性數(shù)據(jù)分析在其跨業(yè)務(wù)問(wèn)題的應(yīng)用中，機(jī)器學(xué)習(xí)也稱為預(yù)測(cè)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.