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.” 一个程序被认为能从经验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后，经过P评判， 程序在处理 T 时的性能有所提升。
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.
