Expectation Maximization and Clustering

This lecture covers the Expectation Maximization algorithm and clustering techniques, focusing on topics such as Gibbs Sampling, detailed balance, posterior inference, and simulated annealing. The slides discuss the process of updating configurations, balancing probabilities, and the idea of clustering data points into groups based on similarity.