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.