Problem 2 (written) - 20 points iid = (a) You have data (Li, Yi) for i 1, ..., n, where x e Rd and y E R. You model this as Yi 129 N(x?w, o?). You use the data you have to approximate w with Wrr (^I + XT X)-1 XT y, where X and y are defined as in the lectures. Derive the results for E[WRR) and V[wrR] given in the slides. (b) If Wrr is the ridge regression solution and Wis is the least squares solution for the above problem, derive an equation for writing WrR as a function of Wls and the singular values and right singular vectors of feature matrix X. Recall that the singular value decomposition of X = USVT. Problem 2 (written) - 20 points iid = (a) You have data (Li, Yi) for i 1, ..., n, where x e Rd and y E R. You model this as Yi 129 N(x?w, o?). You use the data you have to approximate w with Wrr (^I + XT X)-1 XT y, where X and y are defined as in the lectures. Derive the results for E[WRR) and V[wrR] given in the slides. (b) If Wrr is the ridge regression solution and Wis is the least squares solution for the above problem, derive an equation for writing WrR as a function of Wls and the singular values and right singular vectors of feature matrix X. Recall that the singular value decomposition of X = USVT