Exploring Expectation Maximization Algorithm and Maximum Likelihood Estimation

The chapter delves into the expectation maximization algorithm in unsupervised learning, emphasizing hidden states and sensory data relationships, estimation of unknown parameters through iterative processes, challenges of convergence, and its broader perspective within variational inference. It further discusses maximum likelihood estimation in statistical techniques and its application in predictive coding and active inference for estimating unknown variables efficiently.