Consider the AdaBoost algorithm summarized in the textbook and discussed in class. Recall that this algorithm updates weights for each training example in response to how accurately the base (weak) classifier performed on this example in the previous iteration. Suppose that we start with a uniform weight distribution for 20 data points (each point is weighted 0.05). Describe how the weights will be adjusted after one iteration of AdaBoost if the weak classifier is a weighted Majority Classifier. In this example, 18 examples belong to the positive class and 2 examples belong to the negative class.

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You need to design a circuit that implements the functions in the following table: s0 s1 function0 0 a + 10 1 a – b1 0 a + b1 1 a – 1s0 and s1 are 1-bit control inputs to select the function of the circuit. inputs a and b are 4-bitnumbers in 2s complement form. the output is also a 4-bit number in 2s complement form.you are allowed to use only one ttl 7483 4-bit adder to implement all the functions. but anynumber of other components (except the adder) can be used.hint: design a combinational logic circuit to modify the input b and the “carry input” of theadder depending on the control inputs s0 and s1.important: lab grade will depend on the working of the circuit & will be checked of by your labinstructor.1. is the output valid for the following input combinations: a. s0 = 0, s1 = 0, a = 7, b = 3? b. s0 = 0, s1 = 1, a = 7, b = 3? c. s0 = 1, s1 = 0, a = -4, b = -5? d. s0 = 1, s1 = 1, a = -8, b = 6? 2. what is the range of inputs (for both a and b) that will produce the valid output for all the functions?

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