开始学习机器学习，之前在网上找了好久的资料，发现还是吴恩达讲的机器学习更容易被理解。现在网易云课堂出了中文字幕版的吴恩达机器学习了。同时，我推荐github上的一个项目：AiLearning，可以跟着这个学习。
开始正文
第一章是绪论，初识机器学习，讲了三个点：
什么是机器学习
定义（总共给了两个定义，但我觉得只要记得最新的一个定义就可以了）: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.
示例
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.
分类：Supervised learning and Unsupervised learning.
监督学习
定义&应用场景： 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.
PS：其他的定义我也看了，还是吴恩达教授的原话说的更容易让人明白
实质：Supervised learning problems are categorized into "regression" and "classification" problems.
In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.
In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories
将监督学习分为回归问题和分类问题两大类：主要的依据就是数据是否可连续性的表达无监督学习
定义&应用场景： 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.
实质：We can derive this structure by clustering the data based on relationships among the variables in the data. With unsupervised learning there is no feedback based on the prediction results.
示例
Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables,such as lifespan, location, roles, and so on.
Non-clustering: The "Cocktail Party Algorithm", allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).
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