Probabilistic Graphical Models 1: Representation
Master advanced probabilistic modeling using Bayesian networks and Markov networks for complex AI applications.
Master advanced probabilistic modeling using Bayesian networks and Markov networks for complex AI applications.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Probabilistic Graphical Models Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Represent complex probabilistic models using Bayesian and Markov networks
Analyze independence properties in graphical models
Implement temporal models using HMMs and DBNs
Design decision-making systems using influence diagrams
Apply PGMs to real-world machine learning problems
Skills you'll gain
This course includes:
8.8 Hours PreRecorded video
12 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 7 modules in this course
This rigorous course explores probabilistic graphical models (PGMs) as a framework for encoding probability distributions over complex domains. Students learn both directed (Bayesian Networks) and undirected (Markov Networks) representations, their theoretical properties, and practical applications. The curriculum covers advanced topics including template models, structured CPDs, and decision theory, with hands-on programming assignments in the honors track.
Introduction and Overview
Module 1 · 1 Hours to complete
Bayesian Network (Directed Models)
Module 2 · 11 Hours to complete
Template Models for Bayesian Networks
Module 3 · 1 Hours to complete
Structured CPDs for Bayesian Networks
Module 4 · 11 Hours to complete
Markov Networks (Undirected Models)
Module 5 · 17 Hours to complete
Decision Making
Module 6 · 22 Hours to complete
Knowledge Engineering & Summary
Module 7 · 53 Minutes to complete
Fee Structure
Instructor
Pioneer in Machine Learning and Online Education
Professor Daphne Koller has been a faculty member at Stanford University since 1995, currently serving as the Rajeev Motwani Professor in the School of Engineering. Her research focuses on employing machine learning and probabilistic methods to model and analyze complex systems, with current projects in computational biology, computational medicine, and the semantic understanding of physical environments through sensor data. With over 200 refereed publications in prestigious venues such as Science and numerous AI and Computer Science journals, she is a recognized leader in her field, having delivered keynote speeches at over ten major conferences. Koller has received numerous accolades, including the Arthur Samuel Thesis Award, the Sloan Foundation Faculty Fellowship, and the MacArthur Foundation Fellowship. As the founder of CURIS, Stanford's summer research program for undergraduates in computer science, she has trained over 500 students. Additionally, she was instrumental in pioneering Stanford's online education model, leading to the creation of publicly accessible online courses.
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4.6 course rating
1,429 ratings
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