hypohesis = x0 +theta1 *(x1^2) +theta2*(x2^2))
Besides gradient decent , today i would introduce a new method to minimize the cost function. That's called normal equation(It's only useful to the linear regression problem) In order to apply to this method , we need to .
First we define :
X is called design matrix , then we can the following formular .
The normal quation method is useful when numbers of the samples and features are both not too large. Beacause theta needed to calculate the inverse of the matrix.
