In the case of normally distributed classes, discriminant functions are linear (straight lines, planes, and hyperplanes for two-, three-, and n-dimensional feature vectors, respectively) when the covariances matrices of corresponding classes are equal. Confirm this by deriving discriminant functions for a binary classification problem.Given:Prove that linear discriminant functionsAnd decision boundary g(x) = g1(x) - g2(x) = 0 is given by(Hint: Use equations 3.61-3.62 in the textbook)Part 2Perform two iterations of the gradient algorithm to find the minima ofE(w)=2w21+2w1w2+5w22The starting point isw=[2-2]T