= 0 2 1 Expectation Maximization (4P) A dataset is clustered by the EM algorithm into three Gaussian clusters C1, C2 and C3. Cluster C has mixing coefficients T1 = 0.5 with mean 41 = (2, 2) and covariance matrix , (3 3). C. C2 has mixing coefficients T2 = 0.2 with mean y2 = (5,3) and covariance matrix L2 = (* ). C has mixing coefficients #3 = 0.3 with mean ps = (1,4) and covariance matrix Is = (5 2). Now, given a new point p = (3.5,3.5), assign it to one of the clusters by comparing the responsibility of each cluster for p. Write the three responsibility values and all intermediate steps. (Use i = 3.141, e = 2.718 and keep three digits after decimal for all calculation.) 16 0 = 0 2 1 Expectation Maximization (4P) A dataset is clustered by the EM algorithm into three Gaussian clusters C1, C2 and C3. Cluster C has mixing coefficients T1 = 0.5 with mean 41 = (2, 2) and covariance matrix , (3 3). C. C2 has mixing coefficients T2 = 0.2 with mean y2 = (5,3) and covariance matrix L2 = (* ). C has mixing coefficients #3 = 0.3 with mean ps = (1,4) and covariance matrix Is = (5 2). Now, given a new point p = (3.5,3.5), assign it to one of the clusters by comparing the responsibility of each cluster for p. Write the three responsibility values and all intermediate steps. (Use i = 3.141, e = 2.718 and keep three digits after decimal for all calculation.) 16 0