Linear Regression with Multiple Variables

Linear Regression with Multiple Variables

Notation:

• m = number of training examples
• n = number of features
• $x^{(i)}$ = input (features) of $i^{th}$ training example
• $x_j^{(i)}$ = value of feature j in $i^{th}$ training example

Hypothesis function: for Multivariate linear regression in vectored form

$\begin{align}h_a{(x)} & = a_0{x_0} + a_1{x_1} + ... + a_n{x_n} \\ & = a^T x \\ & where \space  x_{0}^{(i)} =1 \text{ for } (i\in { 1,\dots, m } ) \end{align}$ (a transpose times x)