Problem 3 - Linear Discriminant Analysis: Consider the categorical learning problem consisting of a data set with two labels: Label 1: X1 X2 3.81 0.23 3.05 0.68 2.67 -0.55 3.37 3.53 1.84 2.74 Label 2: X; -2.04 -0.72 -2.46 -3.51 -2.05 X2 -1.25 -3.35 -1.310.13 -2.82 (1) For each label above, the data follow a multivariate normal distribution Normal(ui, ) where the covariance E is the same for both label 1 and for label 2. Fit a pair of Guassian discriminant functions to the labels by computing the covariances, means, and proportions of datapoints as discussed in the Linear Discriminant Analysis section. You may use a computer, but you should not use an LDA solver. You should report the values for fi and E. (2) Give the formula for the line forming the discretion boundary. Problem 3 - Linear Discriminant Analysis: Consider the categorical learning problem consisting of a data set with two labels: Label 1: X1 X2 3.81 0.23 3.05 0.68 2.67 -0.55 3.37 3.53 1.84 2.74 Label 2: X; -2.04 -0.72 -2.46 -3.51 -2.05 X2 -1.25 -3.35 -1.310.13 -2.82 (1) For each label above, the data follow a multivariate normal distribution Normal(ui, ) where the covariance E is the same for both label 1 and for label 2. Fit a pair of Guassian discriminant functions to the labels by computing the covariances, means, and proportions of datapoints as discussed in the Linear Discriminant Analysis section. You may use a computer, but you should not use an LDA solver. You should report the values for fi and E. (2) Give the formula for the line forming the discretion boundary