Please help me, please use Matlab.

3. Suppose that we have two classes with prior probabilities p(C1) and p(C2), and that we wish to design a classifier for these classes based on a two dimensional feature vector, x, we assume that the class conditional densities are given by Gaussians, p(x C1) and p(x C2), which have means mi, mi and covariance matrices and . . For the following cases, give (a) The equation for the decision boundary (b) A plot showing the decision boundary, the location of the two means, and the covariance ellipses of the two classes where x''s-1 . 1 0P(C1) 0.5 wherel0PC2) 0.5 0 P(C1) 0.5 P(C2) 0.5 2 3. Suppose that we have two classes with prior probabilities p(C1) and p(C2), and that we wish to design a classifier for these classes based on a two dimensional feature vector, x, we assume that the class conditional densities are given by Gaussians, p(x C1) and p(x C2), which have means mi, mi and covariance matrices and . . For the following cases, give (a) The equation for the decision boundary (b) A plot showing the decision boundary, the location of the two means, and the covariance ellipses of the two classes where x''s-1 . 1 0P(C1) 0.5 wherel0PC2) 0.5 0 P(C1) 0.5 P(C2) 0.5 2