MSE \"Minimizer\" 5. {10 points) Recall from calculus that given some function rs), the I you get from solving % 2 l} is called a critical point of g this means it could be a minimize]: or a maximiser for g. In this question, we will explore some basic properties and build some intuition on why, for certain loss functions such as the MSE lass, the critical point of the loss will always be the minimizer of the loss. Given some linear model x} 2 \"ya: for some real scalar 1*, we can write the the mean squared error (MSE) loss of the model 3" given the observed data {311, gig-hi = 1, . . . ,n as $20.92 - \"r392- i=1 (a) [1 point) Let's break the loss function above into individual terms. Complete the following sentence by lling in the blanks using one of the options in the parenthesis following each of the blanks: The MSE loss function can be viewed as a sum of a _____ [linearj'quadratic/logr arithmic/exponential} terms1 each of which can be treated as a function of ____ {meta-fa)