Explore probabilistic methods for classification in this comprehensive lecture by Gideon Mann from the Center for Language & Speech Processing at Johns Hopkins University. Delve into supervised machine learning techniques, covering topics such as information extraction, semisupervised learning, and document classification. Learn about Naive Bayes, maximum likelihood estimation, and conditional log-linear models. Examine graphical models, including Maximum Entropy Models and Conditional Random Fields. Understand gradient-based optimization, hidden Markov models, and dependency parsing. Investigate advanced concepts like the Generalized Expectations Criteria, KL Divergence, and label regularization. Gain valuable insights into the theoretical foundations and practical applications of probabilistic classification methods in natural language processing and machine learning.