机器学习的定义##
定义一：
the field of study that gives computers the ability to learn without being explicitly programmed
通俗来讲：机器学习就是让计算机有学习没有明确编程能力。也就是说不用通过一行一行的代码，计算机也能够知道要做什么。
定义二：
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.
这个定有有点绕，举个简单的例子：阿法狗通过不断跟别人下棋(T)，获得经验不断升级(E),然后打败的李世石(P).
简单的说：机器学习就是通过不断的训练，然后提升经验，最终做出决策。
分类：
机器学习的问题基本上可以分为一下两类：
a.Supervised learning 监督学习
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
在监督学习中，我们首先会给出一个数据集并且我们已经知道输出会是怎么样的，或者是我们知道输入与输出之间的关系。
b.Unsupervised learning 非监督学习
Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables .
非监督学习允许我们对要处理的问题一无所知或者不知道结果是什么。我们可以从那些我们不必要知道影响变量的数据中推导出结构。
