Module #1 Introduction to Probabilistic Graphical Models Overview of probabilistic graphical models, their applications, and importance
Module #2 Probability Theory Basics Review of probability theory, including Bayes theorem, conditional independence, and random variables
Module #3 Graph Theory Basics Introduction to graph theory, including graphs, nodes, edges, and graph representations
Module #4 Bayesian Networks Definition and properties of Bayesian networks, including directed acyclic graphs (DAGs) and conditional probability tables (CPTs)
Module #5 Inference in Bayesian Networks Exact and approximate inference methods in Bayesian networks, including variable elimination and belief propagation
Module #6 Learning Bayesian Networks Methods for learning Bayesian networks from data, including structure learning and parameter learning
Module #7 Markov Networks Definition and properties of Markov networks, including undirected graphs and factor graphs
Module #8 Inference in Markov Networks Exact and approximate inference methods in Markov networks, including Gibbs sampling and belief propagation
Module #9 Learning Markov Networks Methods for learning Markov networks from data, including structure learning and parameter learning
Module #10 Conditional Random Fields (CRFs) Definition and properties of CRFs, including undirected graphical models and discriminative learning
Module #11 Inference in CRFs Exact and approximate inference methods in CRFs, including Gibbs sampling and belief propagation
Module #12 Learning CRFs Methods for learning CRFs from data, including structure learning and parameter learning
Module #13 Factor Graphs Definition and properties of factor graphs, including bipartite graphs and message passing
Module #14 Inference in Factor Graphs Exact and approximate inference methods in factor graphs, including sum-product algorithm and belief propagation
Module #15 Variational Inference Introduction to variational inference, including mean-field approximation and variational bound
Module #16 Monte Carlo Methods Introduction to Monte Carlo methods, including importance sampling and Markov chain Monte Carlo (MCMC)
Module #17 Applications of Probabilistic Graphical Models Applications of PGMs in computer vision, natural language processing, and robotics
Module #18 Advanced Topics in PGMs Advanced topics in PGMs, including deep probabilistic graphical models and probabilistic programming
Module #19 PGMs in Real-World Scenarios Case studies of PGMs in real-world scenarios, including recommender systems and medical diagnosis
Module #20 PGMs Software and Tools Overview of popular software and tools for building and working with PGMs, including PyMC3 and pgmpy
Module #21 PGMs and Deep Learning Relationship between PGMs and deep learning, including Bayesian neural networks and probabilistic generative models
Module #22 PGMs and Reinforcement Learning Relationship between PGMs and reinforcement learning, including probabilistic planning and decision-making
Module #23 PGMs and Causal Inference Relationship between PGMs and causal inference, including causal Bayesian networks and causal Markov models
Module #24 PGMs and Time-Series Analysis Relationship between PGMs and time-series analysis, including probabilistic graphical models for temporal data
Module #25 PGMs and Uncertainty Quantification Relationship between PGMs and uncertainty quantification, including Bayesian uncertainty estimation and robustness analysis
Module #26 PGMs and Explainability Relationship between PGMs and explainability, including interpretable PGMs and model interpretation techniques
Module #27 PGMs and Ethics Ethical considerations in PGMs, including fairness, transparency, and accountability
Module #28 PGMs and Real-World Challenges Challenges and limitations of PGMs in real-world applications, including scalability, interpretability, and robustness
Module #29 Future Directions in PGMs Future research directions in PGMs, including new applications, methods, and tools
Module #30 Course Wrap-Up & Conclusion Planning next steps in Probabilistic Graphical Models career
