Q1. (5 points) Answer the following with a yes or no along with proper justification. a. Is the decision boundary of voted perceptron linear? b. Is the decision boundary of averaged perceptron linear? Q2. (5 points) Consider the following setting. You are provided with n training examples: (x1, Vi, hi), (x2, y2, h2), . (rn, Vn, hn), where r, is the input example, y is the class label (+1 or -1), and hi >0 is the importance weight of the example. The teacher gave you some additional information by specifying the importance of each training example. How will you modify the perceptron algorithm to be able to leverage this extra information? Please justify your answer where T 1S