Matlab programming

a.Generate two data sets X1 and X'1 of N=200 two-dimensional vectors each. The first half of the vectors stem from the normal distribution with m1=[-5,0]T and S1=I, while the second half of the vectors stem form the normal distribution with m1=[5,0]T and S1=I, where i is the identity 2*2 matrix. Append each vector of both X1 and X'1 by inserting an additional coordinate, which is set equal to 1.

b.....

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3.1 a. Generate two data sets X1 and X of N = 200 two-dimensional vectors each. The first half of the vectors stem from the normal distribution with m1 = [-5, 0] and $1 = 1, while the second half of the vectors stem from the normal distribution with m1 = [5, 0] and $1 = 1, where / is the identity 2 X 2 matrix. Append each vector of both X1 and X, by inserting an additional coordinate, which is set equal to 1. b. Apply the perceptron algorithm, the sum of error squares classifier, and the LMS algorithm on the previous data set, using various initial values for the parameter vector (where necessary). c. Measure the performance of each one of the above methods on both X1 and X1 . d. Plot the data sets X1 and X1 as well as the line corresponding to the parameter vector w. 3.2 Repeat experiment 1 using now the sets X2 and X2 whose first half of their vectors stem from the normal distribution with ml = [-2, 0] and $1 = 1, while the second half of their vectors stem from the normal distribution with m1 = [2, 0] and $1 = 1. 3.3 Repeat experiment 3.1 using now the sets X3 and X3 whose first half of the vectors stem from the normal distribution with m1 = [-1, 0] and $1 = 1, while the second half of the vectors stem from the normal distribution with m1 = [1, 0] and $1 = 1. 3.4 Discuss the results obtained by the previous experiments