5. Let X Rnxd be a data matrix, consisting of n samples, each of which has d features, and let y E R" be a vector of outcomes. For example, each row of X could have information about a house on the market, like its area, number of floors, number of bathrooms/bedrooms, etc., and each entry of y could be the price of that house. We are interested in building a model that predicts house prices from the set of its features, as listed above. Suppose that domain knowledge tells us that the relationship between the features and outcomes is linear; ideally, there exists a set of parameters E R' such that Xt-y. However, n is large and there is noise in the acquisition of X and y, so this system is overdetermined. Still, we wish to find the best linear approximation, i.e. we want to find the that minimizes the loss L(9) = 1 Assuming X has full column rank, compute *-arg mine L(8) in terms of X and y. lly-X 3. 2