Learning Path

Probability Theory Foundations → Advanced Topics in Graphical Models

This advanced learning path equips machine learning professionals with the theoretical foundations and practical skills to model complex relationships using probabilistic graphical models (PGMs). Learners will master Bayesian networks, Markov random fields, conditional random fields, latent Dirichlet allocation, and key inference techniques including variational inference. The path emphasizes genuine prerequisite dependencies and practical applications, enabling learners to design, implement, and reason with PGMs.