Problem 3. Suppose we have two training examples with (6), $1 = where y1 = 1, S2 = We consider linear models for regression. According to the lecture slides, the objective

function can be written as (¹), f(x) = (x¹ST Sx x¹ ST Sx − 2x¹ S¹y+y¹y), - Y/2 = -1. 1 S = = (19), y=(-¹1). We apply gradient descent to minimize the above objective function. In more details, let - (8), n = 1, and we update the model by xk+1x vf(x¹). Please write down x¹ and x². Please give details in the process. (10 marks)