Gradient descent is used to find the linear regression model for the five

points below:

$(1,2),(2,1),(3,2),(4,2),(5,3).$

The initial linear model is

$h_{}={}_0+{}_{l}x=x({}_0=0,{}_l=1),=0\mathrm{.}1,m=5$

Please compute the updated linear regression model after the $1^{st}$ iteration.

${}_0$:$={}_0-\frac{1}{m}{\displaystyle \underset{i=1}{\overset{m}{}}}(h_{}(x^{(i)})-y^{(i)})$

${}_1$:$={}_1-\frac{1}{m}{\displaystyle \underset{i=1}{\overset{m}{}}}(h_{}(x^{(i)})-y^{(i)})*x^{(i)}$

Please compare Gradient Descent and Normal Equation methods for linear

regression.

Please explain how the learning rate $$ affects the Gradient Descent method.

Please explain why feature scaling is needed for a multiple feature

regression problem.