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
About this Course
Skills you will gain
- Bayesian Network
- Graphical Model
- Markov Random Field
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)
- 5 stars74.66%
- 4 stars17.72%
- 3 stars5.33%
- 2 stars0.99%
- 1 star1.28%
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
A comprehensive introduction and review of how to represent joint probability distributions as graphs and basic causal reasoning and decision making.
Excellent course, the effort of the instructor is well reflected in the content and the exercices. A must for every serious student on (decision theory or markov random fields tasks.
Top notch course! I only wish the explanations for answer choices in the quizzes/exams were more elaborate, as some of them are single sentences that don't really provide justification.
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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