https://wiki.devclub.in/index.php?action=history&feed=atom&title=COL776 2026-04-09T10:40:37Z Revision history for this page on the wiki MediaWiki 1.45.1 https://wiki.devclub.in/index.php?title=COL776&diff=606&oldid=prev 2026-03-04T10:00:24Z

<p>Creating course page via bot</p> <p><b>New page</b></p><div>{{Infobox Course<br /> | code = COL776<br /> | name = Learning probabilistic Graphical Models<br /> | credits = 4<br /> | credit_structure = 3-0-2<br /> | pre_requisites = MTL106 OR Equivalent<br /> | overlaps = <br /> }}<br /> <br /> == COL776 : Learning probabilistic Graphical Models ==<br /> Basics: Introduction. Undirected and Directed Graphical Models. Bayesian Networks. Markov Networks. Exponential Family Models. Factor Graph Representation. Hidden Markov Models. Conditional Random Fields. Triangulation and Chordal Graphs. Other Special Cases: Chains, Trees. Inference: Variable Elimination (Sum Product and Max-Product). Junction Tree Algorithm. Forward Backward Algorithm (for HMMs). Loopy Belief Propagation. Markov Chain Monte Carlo. Metropolis Hastings. Importance Sampling. Gibbs Sampling. Variational Inference. Learning: Discriminative Vs. Generative Learning. Parameter Estimation in Bayesian and Markov Networks. Structure Learning. EM: Handling Missing Data. Applications in Vision, Web/IR, NLP and Biology. Advanced Topics: Statistical Relational Learning, Markov Logic Networks.</div>

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