线性回归

Notation:

  m = Number of training examples

  x's = "input" variable / features

  y's = "output" variable / "target" variable

  (x, y) - one training example

  (x⁽ⁱ⁾, yⁱⁱ⁾) - i th training example

符号

  m = 训练样本的数量

  x's = “输入”变量/特征

  y's = “输出”变量/“目标”变量

  (x, y) - 一个训练样本

  (x⁽ⁱ⁾, yⁱⁱ⁾) - 第 i 个训练样本

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Training set of housing prices

房价预测的数据集

Size in feet2(x)	Price in 1000's(y)
2104	460
1416	232
1534	315
852	178

x⁽¹⁾ = 2104; x⁽²⁾ = 1416; y⁽¹⁾ = 460

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How supervised learning work?

监督学习是如何工作的

h 叫 hypothesis 是历史原因

How do we represent h?

如何表示h

[{h_ heta }left( x ight) = { heta _0} + heta_1 {x_1}]

h_θ(x) shorthand: h(x)

h_θ(x) 简写 h(x)

Linear regression with one variable

一个变量的线性回归

Univariate linear regression

单变量线性回归