Please explain as best you can how to get the answer(with theory) and I will give you a thumbs up. Don't simply use chat GPT.

A Gaussian classifier has the following mean vectors and covariance matrices: Class1:Class2:1=[00]2=[50]1=[2112]2=[2112] (i) Sketch the contours of equal likelihoods and the decision boundary produced by the classifier. (7 marks) (ii) Assume that the Bayes' theorem is used for the classification. Sketch the decision boundary of the classifier if the prior probability of Class 1 is 0.1 , i.e., P(C1)=0.1. (6 marks)