Expectation-Maximization Algorithm

This lecture covers the Expectation-Maximization (EM) algorithm, which provides an elegant method to optimize problems involving two-step procedures. It explains the computation of assignments, marginal likelihood, and updates for parameters like mean and covariance. The lecture also discusses the posterior distribution and the challenges in computing maximum likelihood for Gaussian mixture models.