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Explore undirected graphical models and their applications in sequence analysis through this comprehensive lecture by Fernando Pereira from the Center for Language & Speech Processing at Johns Hopkins University. Delve into the mathematical foundations and practical implementations of these probabilistic models, examining how they can be effectively applied to analyze sequential data structures. Learn about the theoretical underpinnings of undirected graphs in machine learning contexts, understand their advantages over directed models for certain sequence analysis tasks, and discover real-world applications in natural language processing and speech recognition. Gain insights into parameter estimation techniques, inference algorithms, and the computational considerations involved in working with these models for sequence-based problems.
