README.md

Machine Learning Introduction

Machine Learning(The reason why machine learning is popular)

- Grew out of work in AI
- New capability for computers

Examples:

- Database mining
    Large datasets from growth of automation/web.
    E.g., Web click data,medical records,biology,engineering
- Applications can't program by hand.
    E.g., Autonomous helicopter,handwriting recognition,most of Natural Language Processing(NLP), Computer Vision.
- Self-customizing programs
    E.g.,Amazon,Netflix product recommendations

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.

Machine learning algorithms:

- Supervised learning
- Unsupervised learning

Others:

- Reinforcement learning
- Recommender systems

Also talk about:

- Practical advice for applying learning algorithms

机器学习简介

机器学习流行的原因

 - AI 行业的高速发展
 - 机器学习已经发展成为计算机的一项新能力

例子：

 - 数据挖掘
     自动化/网络增长的大数据集。
     例如，Web点击数据，医疗记录，生物学，工程学
 - 应用程序本身无法手动编程
     例如，自主直升机，手写识别，大部分的自然语言处理（NLP），计算机视觉。
 - 自定义程序
     亚马逊，Netflix等产品推荐

什么是机器学习？

 提供机器学习的两个定义:

亚瑟·塞缪尔（Arthur Samuel）将其描述为：“学习领域，让计算机能够学习而不被明确编程”。 这是一个较老的非正式的定义。

汤姆·米切尔（Tom Mitchell）提供了一个更现代化的定义："如果一个计算机程序针对某类任务T的性能用P衡量，且根据经验E来自我完善，那么我们称这个计算机程序在从经验E中学习，针对某类任务T，它的性能用P来衡量。"

 示例：玩跳棋。
 E = 打很多棋子的经验
 T = 玩跳棋的任务。
 P = 程序将赢得下一场比赛的概率。

机器学习算法：

 - 监督学习
 - 无监督学习

其他：

 - 加强学习
 - 推荐系统

还谈谈：

 - 应用学习算法的实用建议