Course on Probabilistic Graphical Models by Stanford University [Online, 4 Months]: Enroll Now

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Probabilistic graphical models (PGMs) are 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 sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more.

They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

Skills you gain
  • Inference
  • Bayesian Network
  • Belief Propagation
  • Graphical Model
There are 3 Courses in this Specialization
  • Probabilistic Graphical Models 1: Representation: Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other.
  • Probabilistic Graphical Models 2: Inference
  • Probabilistic Graphical Models 3: Learning
About Stanford University

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.

To enroll for this course, click the link below.

Course on Probabilistic Graphical Models by Stanford University

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