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
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)
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