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.
Offered By
Probabilistic Graphical Models SpecializationStanford UniversityAbout this Course
Learner Career Outcomes
17%
started a new career after completing these courses
18%
got a tangible career benefit from this course
18%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
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Course 2 of 3 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 38 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Skills you will gain
InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation
Learner Career Outcomes
17%
started a new career after completing these courses
18%
got a tangible career benefit from this course
18%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 3 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 38 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Offered by
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.
Syllabus - What you will learn from this course
25 minutes to complete
Inference Overview
This module provides a high-level overview of the main types of inference tasks typically encountered in graphical models: conditional probability queries, and finding the most likely assignment (MAP inference).
25 minutes to complete
2 videos (Total 25 min)
1 hour to complete
Variable Elimination
This module presents the simplest algorithm for exact inference in graphical models: variable elimination. We describe the algorithm, and analyze its complexity in terms of properties of the graph structure.
1 hour to complete
4 videos (Total 56 min)
18 hours to complete
Belief Propagation Algorithms
This module describes an alternative view of exact inference in graphical models: that of message passing between clusters each of which encodes a factor over a subset of variables. This framework provides a basis for a variety of exact and approximate inference algorithms. We focus here on the basic framework and on its instantiation in the exact case of clique tree propagation. An optional lesson describes the loopy belief propagation (LBP) algorithm and its properties.
18 hours to complete
9 videos (Total 150 min)
2 hours to complete
MAP Algorithms
This module describes algorithms for finding the most likely assignment for a distribution encoded as a PGM (a task known as MAP inference). We describe message passing algorithms, which are very similar to the algorithms for computing conditional probabilities, except that we need to also consider how to decode the results to construct a single assignment. In an optional module, we describe a few other algorithms that are able to use very different techniques by exploiting the combinatorial optimization nature of the MAP task.
2 hours to complete
5 videos (Total 74 min)
15 hours to complete
Sampling Methods
In this module, we discuss a class of algorithms that uses random sampling to provide approximate answers to conditional probability queries. Most commonly used among these is the class of Markov Chain Monte Carlo (MCMC) algorithms, which includes the simple Gibbs sampling algorithm, as well as a family of methods known as Metropolis-Hastings.
15 hours to complete
5 videos (Total 100 min)
1 hour to complete
Inference in Temporal Models
In this brief lesson, we discuss some of the complexities of applying some of the exact or approximate inference algorithms that we learned earlier in this course to dynamic Bayesian networks.
1 hour to complete
1 video (Total 20 min)
Reviews
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
by ATAug 22, 2019
Just like the first course of the specialization, this course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.
by ALAug 19, 2019
I have clearly learnt a lot during this course. Even though some things should be updated and maybe completed, I would definitely recommend it to anyone whose interest lies in PGMs.
by LCJul 31, 2018
Very good course. Subject is quiet complex: lack of concrete examples to make sure concepts well understood. Had to review each the Course twice to understand concepts well
by RGMay 15, 2020
Great course. The assignments are old and are not worth doing it. But the content is good for those who are interested in Probabilistic Graphical Models basics.
About the Probabilistic Graphical Models Specialization
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.
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Learning Outcomes: By the end of this course, you will be able to take a given PGM and
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