(3) Consider building a linear perceptron to distinguish between whether a CS 2810 student has done their co-op (Class 1) or not (Class 0). To train the perceptron, we will use a student's age in months (x1) and the number of pets they've owned in their life (x2). The plot below shows the data in vector space, with labels, after standardizing the input features: Each student is described by a vector x=1x1x2, where the initial 1 is a constant bias term. Answer the following questions concerning this linear perceptron. (a) Classify student x=120 with the perceptron w=72.57.5. Please provide the estimate as "Did Co-op" or "Didn't Do Co-op" as well as the group index ( 1 or 0, respectively). (b) In one or two sentences, explain why we add a bias term to each x vector. (c) In one or two sentences, explain whether or not the perceptron will stop training on its own. (d) Determine if you need to update the perceptron w=1157 based on the following student, who has not done their co-op, x=143. If an update is necessary, perform it using =1 and determine if the update fixed the perceptron's classification of the student. (e) Give the equation for the decision boundary of the linear perceptron defined by the vector w=282, give rough sketch of it using the plot provided at the beginning of this question, and discuss in a sentence or two if this decision boundary seems reasonable based on the accuracy of the resulting perceptron