In this problem, each example has only one feature:x ^ (i)=x ^ (i)fori= 1. . . N.

Consider a linear model of the form,

y?wx,

which is a linear model, but with the intercept forced to zero. This occurs in applications where we want to force the predicted valuey? = 0 whenx= 0. For example, if we are modelingy= output power of a motor vs.x= the input power, we would expectx= 0?y= 0.

(a) Given data (x(), y(")) for i E 1. .. N, write a cost function representing the residual sum of squares (RSS) between yi and the predicted value yi as a function of w. (b) Taking the derivative with respect to w, find the w that minimizes the RSS