更改

As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis.

As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis.

作为一项科学努力，机器学习源于对人工智能的探索。在人工智能作为一门学科的早期，一些研究人员对让机器从数据中学习很感兴趣。他们试图用各种符号方法以及当时被称为”神经网络”的方法来处理这个问题; 这些大部分是感知器和其他模型，后来发现这些模型是统计学的广义线性模型的重新发明。概率推理也被使用，特别是在自动医疗诊断中。

作为一项科研成果，机器学习源于对人工智能的探索。在人工智能这一学科研究的早期，一些研究人员对于让机器从数据中进行学习这一问题很感兴趣。他们试图用各种符号方法甚至是当时被称为'''”神经网络 Neural Networks”'''的方法来处理这个问题;但这些方法大部分是感知器或其他模型。后来这些模型随着统计学中广义线性模型的发展而重新出现在大众视野中，与此同时概率推理的方法也被广泛使用，特别是在自动医疗诊断问题上。

However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.

However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.

然而，日益强调的基于知识的逻辑方法导致了人工智能和机器学习之间的裂痕。概率系统一直被数据获取和表示的理论和实际问题所困扰。基于符号 / 知识学习的工作在人工智能中继续进行，导致了归纳逻辑编程，但是更多的统计方面的研究现在已经超出了人工智能本身的领域，在模式识别和信息检索领域。神经网络的研究几乎在同一时间被人工智能和计算机科学所抛弃。这一思路也延续到了人工智能 / 计算机科学领域之外，被来自霍普菲尔德、鲁梅尔哈特和辛顿等其他学科的研究人员称为“连接主义”。他们的主要成功来自于20世纪80年代中期反向传播的重新发明。

然而，日益强调的基于知识的逻辑方法 Knowledge-based approach导致了人工智能和机器学习之间的裂痕。概率系统一直被数据获取和表示的理论和实际问题所困扰。在人工智能内部，符号/知识学习的工作确实在继续，导致了归纳逻辑编程，但更多的统计研究现在已经超出了人工智能本身的领域，即模式识别和信息检索。神经网络的研究几乎在同一时间被人工智能和计算机科学领域所抛弃，但这种思路却在人工智能/计算机之外的领域被延续了下来，被其他学科的研究人员称为“连接主义”，包括霍普菲尔德 Hopfield、鲁梅尔哈特 Rumelhart和辛顿 Hinton。他们的主要成就集中在20世纪80年代中期，在这一阶段神经网络的方法随着反向传播算法的出现而重新被世人所重视。

Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory. As of 2019, many sources continue to assert that machine learning remains a sub field of AI. Yet some practitioners, for example Dr Daniel Hulme, who both teaches AI and  runs a company operating in the field, argues that machine learning and AI are separate. <ref name="elements">

Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory. As of 2019, many sources continue to assert that machine learning remains a sub field of AI. Yet some practitioners, for example Dr Daniel Hulme, who both teaches AI and  runs a company operating in the field, argues that machine learning and AI are separate. <ref name="elements">

机器学习，重组为一个独立的领域，在20世纪90年代开始蓬勃发展。该领域的目标从实现人工智能转变为解决实际性的可解决问题。它将焦点从继承自人工智能的象征性方法转移到借用统计学和概率论的方法和模型上。截至2019年，许多资料继续断言机器学习仍然是人工智能的一个子领域。然而，一些从业者，例如丹尼尔 · 休姆博士，他既教授人工智能，又经营着一家在该领域运营的公司，认为机器学习和人工智能是分开的。Ref name"elements"

机器学习，在重组为一个独立的领域之后，与20世纪90年代开始蓬勃发展。该领域的目标从实现人工智能转变为解决实际中的可解决问题。它将焦点从它从人工智能继承的符号方法转移到从统计学和概率论中借鉴的方法和模型。截止至2019年，许多资料都继续断言机器学习仍然是人工智能的一个子领域。然而，一些该领域的从业者（例如丹尼尔 · 休姆 Daniel Hulme博士，他既教授人工智能，又经营着一家在该领域运营的公司），则认为机器学习和人工智能是分开的。Ref name"elements"

{{cite web

{{cite web

/ 参考

/ 参考

=== Relation to data mining ===

=== Relation to data mining ===