(4) A geneticist believes that two specific gene locations can help classify whether a mouse has dark fur (Class 1 ) or light fur (Class 0). They collect the following small data set (with gene locations already scaled properly): You will help the geneticist by applying a Linear Perceptron to these data. (a) Sketch a to-scale, well-labeled plot of the data, with the decision boundary defined by an initial w=011. (b) Manually run the Linear Perceptron algorithm, using a learning rate of =1. At each iteration of the algorithm: - Specify the slope and intercept of the decision boundary line defined by the current vector w. - Cycle through the data set, beginning with the first observation, calculating the predicted class until you find a misclassified observation. - Update the vector w accordingly. - If you cycle through all six observations without finding a misclassified observation, you have finished. (c) Sketch a to-scale, well-labeled plot of the data, with the decision boundary as defined by the final w