Just do step by step reasoning calculations, draw and sketch it.

Consider the regular 0/1 loss and assume that data is generated according to Py = 0) = P(y = 1) = 1/2. Also assume that the class-conditional densities are Gaussian with mean Mo and covari- ance Eo under class 0, and mean pi and covariance E1 under class 1. For the following two cases, you are asked to i) sketch contours of the level sets of the class conditional densities and label them with p(x|y=0) and p(x|y=1) with x R2; ii) draw the decision boundaries obtained using the Bayes optimal classifier in each case and indicate the regions where the classifier will predict class o and where it will predict class 1. (a) Case 1: po = -M1 and (b) Case 2: Mo = Mi and 2. = [) -[&:] Consider the regular 0/1 loss and assume that data is generated according to Py = 0) = P(y = 1) = 1/2. Also assume that the class-conditional densities are Gaussian with mean Mo and covari- ance Eo under class 0, and mean pi and covariance E1 under class 1. For the following two cases, you are asked to i) sketch contours of the level sets of the class conditional densities and label them with p(x|y=0) and p(x|y=1) with x R2; ii) draw the decision boundaries obtained using the Bayes optimal classifier in each case and indicate the regions where the classifier will predict class o and where it will predict class 1. (a) Case 1: po = -M1 and (b) Case 2: Mo = Mi and 2. = [) -[&:]