catetan..
- Hypothesis function:
$latex h_\theta(x)=\theta_0+\theta_1 x&s=1$
- Cost function:
$latex J(\theta_0,\theta_1)=\frac{1}{2m}\sum\limits_{i=1}^m (h_\theta(x^{(i)}) – y^{(i)})^2&s=1$
- Gradient descent for linear regression
repeat until convergence $latex \{&s=1$
$latex \theta_0:=\theta_0-\alpha\frac{1}{m}\sum\limits_{i=1}^m (h_\theta(x^{(i)}) – y^{(i)})&s=1$
$latex \theta_1:=\theta_1-\alpha\frac{1}{m}\sum\limits_{i=1}^m (h_\theta(x^{(i)}) – y^{(i)}x^{(i)})&s=1$
$latex \}&s=1$
