今天看了coursera上面吴恩达的机器学习课程，虽然是英文的，但是感觉还是挺通俗易懂的，顺便还可以锻炼一下英语听力，可以一举两得。
学习任何一个概念之前，弄清楚这个概念的定义是很重要的，至少以后不会走偏。机器学习有两个定义，一个年纪比较大：
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.
我挺喜欢这个定义的，授机以鱼不如授机以渔，以后教会机器写程序就不用自己写程序了，哈哈。第二个定义比较年轻而正式：
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.
在这个定义里面ML=PET，举个下棋的例子，T（任务）就是下棋，P（表现）就是下棋的胜率，E（经验）就是下了多少盘棋，如果随着下棋经验值的增长，胜率越高就是机器学习了。
