2. (25 points) Consider the following experiment on perceptron learning for random training sets of dimension 10 : - Generate an 11-dimensional weight vector w, where the first dimension is 0 and the other 10 dimensions are sampled independently at random from the uniform (0,1) distribution (the first dimension will serve as the threshold and we set it to 0 for convenience). - Generate a random training set with 100 examples, where each dimension of each training example is sampled independently at random from the uniform {1,1} distribution. The examples are all classified in accordance with w. 1 - Run the perceptron learning algorithm, starting with a zero weight vector, on the training set you just generated. Keep track of the number of iterations it takes to learn a hypothesis that correctly separates the training data