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This lecture continues the exploration of the Expectation-Maximization (EM) algorithm, building upon the foundations established in Part 1. Delve into advanced concepts and applications of this powerful statistical technique used for finding maximum likelihood estimates of parameters in probabilistic models with latent variables. Learn how the EM algorithm iteratively alternates between expectation and maximization steps to converge on optimal parameter values when dealing with incomplete data problems.
