class 2 Using the points from the following table. X1 X2 class Data point -1.2 0.8 class1 Data point 1.1 0.5 2 Data point -0.6 -0.2 class 1 3 Data point 0.9 -1.2 class 2 4 Apply augmentation and normalization to the points and use weights wo=-0.1, w,=0.5,W2=-1, run batch perceptron gradient decent. The batch size should be all four points. (round to two decimal places. Example: If the final answer is 1. please enter 1.00 to the blank) J(w) = (-w+y) YEY VJ (w) = (-y) VEY n(k) = 1 (a) What is the value of V)(w) after the full batch? VJ (w) = [jo, ji, j2] JO ji- 12 A A/ (b)What is the weight matrix after the full batch? w W, = 4 A/ Question 2 (1 point) In intelligent systems, generally, feature extraction is: O An unsupervised method A supervised method O A semi-supervised method None of these Question 3 (1 point) Consider a four-class Bayes classifier. The classes are represented as W1, W2, W3, and W4. The posterior probabilities of classes for a given feature x are given as follows: P(W1|x) = 0.01, P(W2|x) = 0.45, and P(W3x) = 0.2. Based on the minimum-error-rate Bayes classifier, feature x indicates___class. Please enter 1 for W1, 2 for W2, 3 for W3, or 4 for W4. For eg. if your answer is W4 then enter 4. A/ (a) Please keep 2 rounded decimal digits for the decimal numbers of your answers unless specified otherwise in the question. class 2 Using the points from the following table. X1 X2 class Data point -1.2 0.8 class1 Data point 1.1 0.5 2 Data point -0.6 -0.2 class 1 3 Data point 0.9 -1.2 class 2 4 Apply augmentation and normalization to the points and use weights wo=-0.1, w,=0.5,W2=-1, run batch perceptron gradient decent. The batch size should be all four points. (round to two decimal places. Example: If the final answer is 1. please enter 1.00 to the blank) J(w) = (-w+y) YEY VJ (w) = (-y) VEY n(k) = 1 (a) What is the value of V)(w) after the full batch? VJ (w) = [jo, ji, j2] JO ji- 12 A A/ (b)What is the weight matrix after the full batch? w W, = 4 A/ Question 2 (1 point) In intelligent systems, generally, feature extraction is: O An unsupervised method A supervised method O A semi-supervised method None of these Question 3 (1 point) Consider a four-class Bayes classifier. The classes are represented as W1, W2, W3, and W4. The posterior probabilities of classes for a given feature x are given as follows: P(W1|x) = 0.01, P(W2|x) = 0.45, and P(W3x) = 0.2. Based on the minimum-error-rate Bayes classifier, feature x indicates___class. Please enter 1 for W1, 2 for W2, 3 for W3, or 4 for W4. For eg. if your answer is W4 then enter 4. A/ (a) Please keep 2 rounded decimal digits for the decimal numbers of your answers unless specified otherwise in the