Graphical Models Course is designed to teach Graphical Models, fundamentals of Graphical Models, Probabilistic Theories, Types of Graphical Models – Bayesian (Directed) and Markov’s (Undirected) Networks, Representation of Bayesian and Markov’s Networks, Concepts related to Bayesian and Markov’s Networks, Decision Making – theories and assumption, Inference and Learning in Graphical Models.
Understand Graphical models, graph theory, probability theory, components of graphical models, types of graphical models, representation of graphical models, Introduction to inference, learning and decision making in Graphical Models
Understand Bayesian networks, independencies in Bayesian Networks and building a Bayesian networks
Understand Markov’s networks, independencies in Markov’s networks, Factor graph and Markov’s decision process
Structures and Parametrization in graphical Models
Bayesian and Markov’s Networks
Decision-Making – theories and assumption
Learning in Graphical Models.