本文介绍了机器学习的两种定义，并通过玩跳棋的例子解释了如何衡量机器学习的效果。此外，还探讨了机器学习的两大分类：监督学习和非监督学习。
What is Machine Learning?
Two definitions of Machine Learning are offered. Arthur Samuel described it as: "the field of study that gives computers the ability to learn without being explicitly programmed." This is an older, informal definition.
Tom Mitchell provides a more modern definition: "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."
Example: playing checkers.
E = the experience of playing many games of checkers
T = the task of playing checkers.
P = the probability that the program will win the next game.
In general, any machine learning problem can be assigned to one of two broad classifications:
Two definitions of Machine Learning are offered. Arthur Samuel described it as: "the field of study that gives computers the ability to learn without being explicitly programmed." This is an older, informal definition.
Tom Mitchell provides a more modern definition: "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."
Example: playing checkers.
E = the experience of playing many games of checkers
T = the task of playing checkers.
P = the probability that the program will win the next game.
In general, any machine learning problem can be assigned to one of two broad classifications:
Supervised learning and Unsupervised learning.
总结：
机器学习的定义(不存在一个明确的定义)：
①在没给出明确的编程的情况下，让计算机自己学习的领域 -- Arthur Samuel
②一个程序能从经验E中学习，解决任务T,达到性能度量值P,当且仅当，有了经验E之后，经过P的判断，提升程序处理T的性能
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