MACHINE LEARNING:

This is a simple data set to illustrate the Perceptron Learning Model. Consider the following training data with two categories (labels):

C1(1) : (0, 1.5)T (1, 1)T (2, 2)T (2, 0)T

C2(1) : (0, 0)T (1, 0)T (0, 1)T

That is, there are seven training data points, each data point has two features and a corresponding label. (a) Plot these seven training data points and observe that they are separable. (b) Starting with the weights w = (2, 4, 1)T , where w0 = 2 is the threshold with corresponding artificial coordinate x0 = 1. Plot the linear model w T x = 0. Note that, this linear model doesnt separate all data points. (c) Using the Perceptron Learning Algorithm, update the weights so that the linear model will eventually separate all seven data points. At each iteration, plot the linear model on the same plot with the data points. In your write-up, you have to show all performed calculations at each iteration of the perceptron learning algorithm as well as the plots.