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 part of the Probabilistic Graphical Models Specialization
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About this Course
Skills you will gain
- Bayesian Network
- Graphical Model
- Markov Random Field
Offered by
Syllabus - What you will learn from this course
Introduction and Overview
Bayesian Network (Directed Models)
Template Models for Bayesian Networks
Structured CPDs for Bayesian Networks
Markov Networks (Undirected Models)
Decision Making
Reviews
- 5 stars74.60%
- 4 stars17.70%
- 3 stars5.33%
- 2 stars1.06%
- 1 star1.28%
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
A comprehensive introduction and review of how to represent joint probability distributions as graphs and basic causal reasoning and decision making.
Great course. some programming assignments are tough (not too nicely worded and automatic grader can be a bit annoying) but all in all, great course
Superb exposition. Makes me want to continue learning till the very end of this course. Very intuitive explanations. Plan to complete all courses offered in this specialization.
Very well designed. There were areas here I struggled with the technical details and had to read up a lot to understand. The assignments are very well designed.
About the Probabilistic Graphical Models Specialization

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Learning Outcomes: By the end of this course, you will be able to
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