吴恩达Coursera机器学习课程笔记-定义分类

What is Machine Learning

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“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.”
—Tom Mitchell

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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.

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classifications

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.

1、regression

definition: 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.

examples:
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.

2)Given a picture of Male/Female, We have to predict his/her age on the basis of given picture.

2、classification

definition: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.

examples:
1) Given a picture of Male/Female, We have to predict Whether He/She is of High school, College, Graduate age.

2) Banks have to decide whether or not to give a loan to someone on the basis of his credit history.

Unsupervised Learning

Unsupervised learning, on the other hand, 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.

Example:
1)Clustering: Take a collection of 1000 essays written on the US Economy, and find a way to automatically group these essays into a small number that are somehow similar or related by different variables, such as word frequency, sentence length, page count, and so on.

2)Non-clustering: The “Cocktail Party Algorithm”, which can find structure in messy data (such as the identification of individual voices and music from a mesh of sounds at a cocktail party