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Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required  to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than have human programmers specify every needed step.<ref name = "Alpaydin2020">{{cite book

Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required  to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than have human programmers specify every needed step.<ref name = "Alpaydin2020">{{cite book

机器学习问题涉及到计算机如何实现在没有明确编程策略的情况下完成任务。而对于分配给计算机的简单任务，可以编写算法程序，告诉机器如何执行解决手头问题所需的所有步骤，这种方法对于计算机来说，是不需要“学习”的。而对于更高级的任务，手动创建解决问题的算法对人们目前的编程水平来说是十分困难的。实际上，帮助机器学习、开发自己的算法比让程序员人工指定解决问题的每个步骤更有效率。 2020"{ cite book"

机器学习问题涉及到计算机如何实现在没有明确编程策略的情况下完成任务。而对于分配给计算机的简单任务，可以编写算法程序，告诉机器如何执行解决手头问题所需的所有步骤，这种方法对于计算机来说，是不需要“学习”的。而对于更高级的任务，手动创建解决问题的算法对人们目前的编程水平来说是十分困难的。实际上，帮助机器学习、开发自己的算法比让程序员人工指定解决问题的每个步骤更有效率。 2020"

{ cite book"

  | author = Ethem Alpaydin  

  | author = Ethem Alpaydin  

{} / ref

{} / ref

 

<br/>Reinforcement learning:  A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent) As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximise.  

<br/>Reinforcement learning:  A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent) As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximise.  

Br / '''强化学习 Reinforcement learning''': 计算机程序与某动态环境相互作用，同时在这个环境中它必须执行一个特定的目标（比如驾驶一辆车或者与对手玩一个游戏）。当它定位到它正确的问题空间时，程序会被提供类似于奖励的反馈，而算法的目标就是将奖励最大化。

'''强化学习 Reinforcement Learning''': 计算机程序与某动态环境相互作用，同时在这个环境中它必须执行一个特定的目标（比如驾驶一辆车或者与对手玩一个游戏）。当它定位到它正确的问题空间时，程序会被提供类似于奖励的反馈，而算法的目标就是将奖励最大化。

Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P,  improves with experience E."<ref name="Mitchell-1997">{{cite book

Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P,  improves with experience E."<ref name="Mitchell-1997">{{cite book

汤姆 · 米切尔 Tom M. Mitchell对机器学习领域研究的算法提供了一个被广泛引用的、更为正式的定义: “一个电脑程序要完成任务（T），如果电脑获取的关于T的经验（E）越多就表现（P）得越好，那么我们就可以说这个程序‘学习’了关于T的经验。” ref name"Mitchell-1997"{ cite book

汤姆 · 米切尔 Tom M. Mitchell对机器学习领域研究的算法提供了一个被广泛引用的、更为正式的定义: “一个电脑程序要完成任务（T），如果电脑获取的关于T的经验（E）越多就表现（P）得越好，那么我们就可以说这个程序学习了关于T的经验。” ref name"Mitchell-1997"{ cite book

|author=Mitchell, T.  

|author=Mitchell, T.  

这个关于机器学习任务的定义提供了一个基本的操作型定义，但并不是通过已知的术语来进行定义的。此前，阿兰 · 图灵 Alan Turing在他的论文《计算机器与智能 Computing Machinery and Intelligence》中提出了“机器能思考吗?”这个问题的替代问题为“机器能做我们(作为思考实体)能做的事情吗？”.

这个关于机器学习任务的定义提供了一个基本的操作型定义，但并不是通过已知的术语来进行定义的。此前，阿兰 · 图灵 Alan Turing在他的论文《计算机器与智能 Computing Machinery and Intelligence》中提出了“机器能思考吗?”这个问题的替代问题为“机器能做我们(作为思考实体)能做的事情吗？”.

 

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 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。

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”。（编者注：连接主义又称为仿生学派或生理学派，其主要原理为神经网络及神经网络间的连接机制与学习算法。）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，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"

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"

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