## Introduction

### 1.1 What is Machine Learning

1. Arthur Samue 提出的定义：

“The field of study that gives computers the ability to learn without being explicitly programmed.”

2. Tom Mitchell 提出的定义：

“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.”

### 1.2 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.

• 在回归问题中，我们尝试预测出连续的输出。
• 在分类问题中，我们尝试预测出离散的输出。

### 1.3 Unsupervised Learning

Unsupervised learning, allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.

## 专业名词整理

• supervised learning：监督学习
• regresssion：回归
• classification：分类
• horizontal axis：横轴、vertical axis：纵轴
• quadratic function：二次函数、cubic function：三次函数
• discrete value：离散值、continuous value：连续值
• training set：训练集、data set：数据集
• unsupervised learning：非监督学习
• cluster：簇、clustering algorithm：聚类算法

## 参考

Coursera-ML-AndrewNg-Notes