(ll'J points) Recall from calculus that given some flmction 53(3),, the a: you get from solving Ed? = I} is called a critical point ofy this means it could be a minimiser or a masimiser for g. In this question} we will explore some basic properties and build some intuition on why} for certain lom functions such as the MSE loss} the critical point of the lom wl always be the minimiser of the loss. IGiven some linear model at) = "(I for some real scalar of, we can write the the mean squared error (LEE) loss of the model f given the observed data {rhyihi = 1} . . . in. as gs. arr)\"- (e) (1 point) Finally, explain why in our case that, when we solve for the critical point of the MSE loss function by taking the gradient with respect to the parameter and setting the expression to 0, it is guranteed that the solution we find will minimize the MSE loss