机器学习概览-优快云博客

学习资料来自Coursea

1 什么是机器学习？

Tom Mitchell给出的定义是：“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.”
Coursea给出的例子：玩跳棋

	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.

个人的理解是，首先用 “p” 来衡量一个计算机程序完成任务T的性能，程序通过对与任务T相关的经验P（如数据）学习，来提高 “p”

总的来说，机器学分为两类

监督学习 (Supervised Learning)
无监督学习 (Unsupervised Learning)

2 监督学习

在监督学习中，我们知道数据集中各个特征（feature)与结果之间的联系

# 原文
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.

监督学习又可以分为两类：

回归问题（Regression Problem)
关键词：连续

在连续的输出中尝试预测结果，寻找输入的变量和输出结果之间的连续函数关系

# 原文
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.

分类问题（Classification Problem)
关键词：离散

在离散的输出结果中预测结果，也就是说，寻找输入和离散的分离类别结果之间的关系。

# 原文
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.

Cousera中给的两个例子
Example 1:

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.

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.

3 无监督学习

无监督学习中，所给予的数据集中，我们并不清楚数据集存在什么字段以及数据集所产生的结果。无监督学习所要做的是从数据集中找寻数据的结构

# 原文
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.

例子：
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).