吴恩达机器学习(一) 定义及算法简单介绍

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.


1.对机器学习的定义

吴恩达机器学习(一) 定义及算法简单介绍_第1张图片

反例:大量if-else组成的判断计算机程序

 

Experience  :    成千上万次对弈/观察用户主动分类  (机器找规律/自己学习的来源)

Task  :  下棋/用户分类  (原始动作/在哪里执行)

Performance  :  胜率/分类正确  (如何衡量学习成功)

吴恩达机器学习(一) 定义及算法简单介绍_第2张图片

Task在反复的进行Experience以后,可以提升Performance

2.机器学习算法

Main:

    Supervised learning 监督学习:(我们教计算机如何做某件事)

    Unsupervised learning 无监督学习:(计算机自己学习如何做某件事情)

Others:

    Reinforcement learning 强化学习

    Recommender systems 推荐系统

 

 

 

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