question (a) (b) (d) is a must

D Consider the instance space consisting of integer points in the x.y plane and the set of hypotheses H consisting of rectangles. More specifically, hypotheses are of the form asxsb,csy sd, where a, b, c, and d can be any integers. Consider further the version space with respect to the set of positive (+) and negative (-) training examples shown in the diagram. 5- 1 .t a) Apply the CANDIDATE-ELIMINATION learning algorithm. Write out the intermediate and the final results. Draw the final result on the diagram. Now assume that you are a teacher, attempting to teach the target concept (3 sx s5,2 sy s9). What is the smallest number of training examples you can provide so that the CANDIDATE-ELIMINATION learning algorithm will perfectly learn the target concept? b) Derive the gradient descent training rule assuming that the target function representation is: c) Define explicitly the cost/error function E, assuming that a set of training examples D is provided, where each training example d E D is associated with the target output td d) Given the target function representation defined in 1c. prove that Least Mean Squares (LMS) training rule performs a gradient descent to minimize the cost/error function E defined in 1c. D Consider the instance space consisting of integer points in the x.y plane and the set of hypotheses H consisting of rectangles. More specifically, hypotheses are of the form asxsb,csy sd, where a, b, c, and d can be any integers. Consider further the version space with respect to the set of positive (+) and negative (-) training examples shown in the diagram. 5- 1 .t a) Apply the CANDIDATE-ELIMINATION learning algorithm. Write out the intermediate and the final results. Draw the final result on the diagram. Now assume that you are a teacher, attempting to teach the target concept (3 sx s5,2 sy s9). What is the smallest number of training examples you can provide so that the CANDIDATE-ELIMINATION learning algorithm will perfectly learn the target concept? b) Derive the gradient descent training rule assuming that the target function representation is: c) Define explicitly the cost/error function E, assuming that a set of training examples D is provided, where each training example d E D is associated with the target output td d) Given the target function representation defined in 1c. prove that Least Mean Squares (LMS) training rule performs a gradient descent to minimize the cost/error function E defined in 1c