In Python:

Write a general function to generate random samples from N (4,2) in d-dimensions (i.e., HER and D E Rdxd). Write a procedure of the discriminant of the following form 9:(x) ==} (x ws)???? (x vi) log(27) 10g (Sil) + log(P(w.)) (1) Generate a 2D dataset with three classes and use the quadratic classifier above to learn the parameters and make predictions. As an example, you should generate training data shown below to estimate the the parameters of the classifier in (1) and you should test the classifier on another randomly generated dataset. It is also sufficient to show the dataset used to train your classifier and the decision boundary it produces. = 4 W3 2 2 0 2 11 Write a procedure for computing the Mahalanobis distance between a point x and some mean vector, given a covariance matrix . Implement the nave Bayes classifier from scratch and then compare your results to that of Python's built-in implementation. Use different means, covariance matrices, prior probabilities (indicated by relative data size for each class) to demonstrate that your implementations are correct. Write a general function to generate random samples from N (4,2) in d-dimensions (i.e., HER and D E Rdxd). Write a procedure of the discriminant of the following form 9:(x) ==} (x ws)???? (x vi) log(27) 10g (Sil) + log(P(w.)) (1) Generate a 2D dataset with three classes and use the quadratic classifier above to learn the parameters and make predictions. As an example, you should generate training data shown below to estimate the the parameters of the classifier in (1) and you should test the classifier on another randomly generated dataset. It is also sufficient to show the dataset used to train your classifier and the decision boundary it produces. = 4 W3 2 2 0 2 11 Write a procedure for computing the Mahalanobis distance between a point x and some mean vector, given a covariance matrix . Implement the nave Bayes classifier from scratch and then compare your results to that of Python's built-in implementation. Use different means, covariance matrices, prior probabilities (indicated by relative data size for each class) to demonstrate that your implementations are correct