1. In the class, we derived the closed-form solution for solving the paramyeters of the linear regression with one output. In this problem, you will apply the same technique to derive the equations for learning the parameters of linear regression with two outputs y E R2 y=(yy/jointly. Given training data D-X[m], y[m]], m-1,2,..,M, derive the equations to learn the regression parameter matrix W-[ Wi, W2], where Wi-[w1, W1ol and W2-[W2, w2ol by minimizing the mean squared errors. 1. In the class, we derived the closed-form solution for solving the paramyeters of the linear regression with one output. In this problem, you will apply the same technique to derive the equations for learning the parameters of linear regression with two outputs y E R2 y=(yy/jointly. Given training data D-X[m], y[m]], m-1,2,..,M, derive the equations to learn the regression parameter matrix W-[ Wi, W2], where Wi-[w1, W1ol and W2-[W2, w2ol by minimizing the mean squared errors