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Probabilistic Graphical Models 2: Inference

Stanford University via Coursera

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  • Provider Coursera
  • $ Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 5 weeks long

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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.

This course is the second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.

Taught by

Daphne Koller

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Reviews for Coursera's Probabilistic Graphical Models 2: Inference
4.3 Based on 3 reviews

  • 5 stars 67%
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  • 3 star 33%
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  • 1
Dmytro A
5.0 a year ago
by Dmytro completed this course, spending 20 hours a week on it and found the course difficulty to be hard.
An immersing, challenging and time-consuming course, just as it's first part. Helped me improve understanding of ML foundations theory. Programming assignments may look a bit archaic, as I see that Matlab/Octave isn't gaining traction in ML community nowadays, but it's a language that's expressive enough, and in no way makes this course boring or worthless. Although it would be nice if was accompanied with assignments in Python or R or Scala.
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Tianpei X
5.0 a year ago
by Tianpei completed this course.
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Stephane M
3.0 a year ago
Stephane completed this course.
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