You are given a feature vector X, a parameter vector W and a label Y. X =[1,x1,x2,x3]=[1,4,9,5] W = [w0, w1, w2, w3] = [0.5, -0.8, 1.0, 0.3] Y=2

Gradient descent is an iterative optimization algorithm used to find the minimum of a loss function. Perform an iteration of gradient descent to find the updated values of the parameters. Assume learning rate, alpha, is 0.01.

{Hint: find predicted value of y and use residual sum of squares to find loss/error. Update values of W using gradient descent, and verify that the loss has decreased with the new set of parameters.}