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 stars74.60%
- 4 stars17.70%
- 3 stars5.33%
- 2 stars1.06%
- 1 star1.28%
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
Prof. Koller did a great job communicating difficult material in an accessible manner. Thanks to her for starting Coursera and offering this advanced course so that we can all learn...Kudos!!
Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.
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.
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
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
Learning Outcomes: By the end of this course, you will be able to
More questions? Visit the Learner Help Center.