我们除 AI 以外，听的最多的如果是机器自学和广度自学了。
那时他们用很难认知的形式如是说机器自学是甚么。易认知版
具体来说，机器自学并非此种具体内容的演算法，而要许多演算法的泛称。
就好似没人说“我讨厌吃水果”，但你却是不晓得他具体内容讨厌吃甚么，即使水果包涵了许多小东西，机器自学也是这般。
机器自学是并非认知呢？
倘若他们已经开始教小学生写字（一、二、三）。他们具体来说会掏出3张卡牌，接着便让小学生看卡牌，默默地说“四条纵线的是一、四条纵线的是二、四条纵线的是三”。
急速多次重复下面的操作过程，小学生的神经系统就在时不时的自学。
当多次重复的单次足够多多时，小学生就专业委员会了两个奥义——重新认识简化字：一、二、三。
他们用下面人类文明的自学操作过程来等效机器自学。机器自学跟下面提及的人类文明自学操作过程很相近。
下面提及的算数的卡牌在机器自学中叫——体能训练集下面提及的“四条纵线，四条纵线”此种界定相同简化字的特性叫——特点下好友急速自学的操作过程叫——可视化专业委员会了写字后归纳出的规律性叫——数学模型透过体能训练集，急速辨识特点，急速可视化，最终逐步形成有效率的数学模型，那个操作过程就叫“机器自学”！
精确版
Machine learning—— Wikipedia
Machine learning (ML) is the study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. 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 the applications of email filtering, detection of network intruders, 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.
扩展阅读
机器自学——百度百科
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