Probabilistic Graphical Models
( 30 Modules )

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

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Probabilistic Graphical Models ( 30 Modules )

Probabilistic Graphical Models
( 30 Modules )