This specialization track is composed of three courses in probabilistic graphical models, that provide a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations is at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning. Knowledge in probabilistic graphical models has many applications in natural language processing, speech recognition, and machine learning problems.
|Offered by: Stanford University||Level: Advanced|
|Location: Online||Type: Free|
|Language: English||License: N/A|
|Gained skills: probabilistic graphical models; Bayesian networks; Markov networks; belief propagation algorithms; MAP algorithms||Target audience: practitioners; data scientists; data analysts; researchers|
|Required skills: probability theory; graph algorithms; machine learning|