Intelligent Systems II · TEE · read-chapter

Unit 1 · Gaussian mixture models and EM

Unit 1 · Mixture models, GMM, expectation maximization, clustering using GMM · 10-mark PYQ in both papers

1. Before GMM: centre, spread, density and likelihood
2. GMM prerequisites: mean, variance, density and mixture
3. EM for GMM: every responsibility and parameter update
4. 10-mark answer: GMM vs k-means, log-likelihood derivation, parameters for K = 5
5. Clustering with GMM: uses, advantages and limits