If someone could help point me in the right direction at least I would be grateful.

Part 1: Perceptron Learning The chart below shows a set of two-dimensional input samples from two classes Class-1 0.9 0.8 0.7 0.6 N 0.5 0.4 0.3 0.2 0.1 O Class 1 o 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 i1 (a) It looks like there exists a perfect classification function for this problem that is linearly separable, and therefore a single perceptron should be able to learn this classification task perfectly. Let us study the learning process, starting with a random perceptron with 0-02, and weights wi 1, and w2 1. For the inputs, just estimate their coordinates from the chart. Now add the perceptron's initial line of division to the chart. How many samples are misclassified? Then pick an arbitrary misclassified sample and perform the computation of the weight update (you can choose -0.1 or any other value ifyon like you can experiment a b! find a value that leads to efficient learning). Illustrate the perceptron's new line of division in the same chart or a different one, and give the number of misclassified samples. Repeat this process two more times so that you have a total of four lines (or fewer if your perceptron achieves perfe classification earlier). You could do the calculation using a simple program (b) Develop a program for perceptron using your experience in step (a). You could use any programming language including Matlab (or Octave) Let the program run until the perceptron achieves perfect classification (indicate the number of steps?) Note: If your perceptron did not reach perfect classification, change the initial values for the