吴恩达机器学习笔记（一）初识机器学习

本文章是笔者根据Coursera上吴恩达教授的机器学习课程来整理的学习笔记。如果是初学者，建议大家首先观看吴恩达教授的课程视频，然后再来看博文的要点总结。两者一起食用，效果更佳。

一、什么是机器学习

第一种定义：
“the field of study that gives computers the ability to learn without being explicitly programmed.”

第二种定义：
“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.

二、监督学习（Supervised Learning）

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.

Supervised learning problems are categorized into “regression” and “classification” problems.

2.1 回归问题（Regression Probblem）

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.

2.2 分类问题（Classification Probblem）

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.

Example 1:
(a) Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.
(b) We could turn this example into a classification problem by instead making our output about whether the house “sells for more or less than the asking price.” Here we are classifying the houses based on price into two discrete categories.

Example 2:
(a) Regression - Given a picture of a person, we have to predict their age on the basis of the given picture
(b) Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.

三、无监督学习（Unsupervised Learning）

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.

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.

Example:
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).

四、Octave工具

Octave中封装好了很多算法函数（如SVM等），因此我们可以首先使用Octave建原型，原型验证成功后再移植到其他的开发环境。
(编者注：这个是多年前吴恩达讲课时的情况，现在python也已经有了很多机器学习和深度学习的库，国内python已经成为机器学习的主流语言)