吴恩达-机器学习（Meachine Learning）-第一周

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为了督促自己进行学习，也为了记录一下要点，还为了补充点博客，毕竟这么久没写几篇博客怪不好意思的。
优快云上有好多比本篇更全面的，本文只做个人记录(从Coursera上复制的)，肯定比较简陋，想看更全的笔记请移步别处，我主要的笔记都在iPad上了。

What is Machine Learning?

What is Machine Learning?
Two definitions of Machine Learning are offered. Arthur Samuel described it as: “the field of study that gives computers the ability to learn without being explicitly programmed.” This is an older, informal definition.

Tom Mitchell provides a more modern definition: “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.

In general, any machine learning problem can be assigned to one of two broad classifications:
Supervised learning and Unsupervised learning.

译文（机翻）：

什么是机器学习？
给出了机器学习的两个定义。亚瑟·塞缪尔（Arthur Samuel）将其描述为：“赋予计算机学习能力而无需明确编程的研究领域。”这是一个更古老、非正式的定义。
Tom Mitchell提供了一个更为现代的定义：“一个计算机程序据说是从经验E中学习关于某类任务T和性能度量P的知识，如果它在T中的任务性能（用P度量）随着经验E的提高而提高。”

例如：跳棋。

E=玩许多跳棋游戏的经验(经验)
T=跳棋的任务。(任务的目的，想要干什么，达成什么效果)
P=程序赢得下一场比赛的概率。(性能度量，比如正确率、赢的概率)

一般来说，任何机器学习问题都可以分为两大类：
有监督学习(Supervised learning)和无监督学习(Unsupervised learning)。

Supervised Learning

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. 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.

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.

译文（机翻）：

有监督学习
在有监督学习中，我们得到一个数据集，并且已经知道我们正确的输出应该是什么样的，有这样一个想法，即输入和输出之间是有关系的。

监督学习问题分为回归问题(Regression)和分类问题(Classification)。
在回归问题中，我们试图在连续输出中预测结果，这意味着我们试图将输入变量映射到某个连续函数。
在分类问题中，我们试图以离散输出预测结果。换句话说，我们试图将输入变量映射到离散的类别中。<