机器学习概念_机器学习(tom m.mitchell)

一、什么是机器学习

1.机器学习：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.

• [ 监督学习]
• [ 非监督学习]

监督学习

在监督学习中，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子的，因为我们知道输入和输出之间存在着一种关系。
监督学习问题分为回归和分类问题。在回归问题中，我们试图预测一个连续输出的结果，这意味着我们试图将输入变量映射到某个连续函数。在一个分类问题中，我们试图在一个离散输出中预测结果。换句话说，