Probabilistic Graphical Models 3: Learning
Master advanced PGM concepts: parameter estimation, structure learning, and EM algorithm for incomplete data.
Master advanced PGM concepts: parameter estimation, structure learning, and EM algorithm for incomplete data.
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
Compute sufficient statistics for PGM learning from data
Implement maximum likelihood and Bayesian parameter estimation
Formulate structure learning as optimization problems
Apply EM algorithm for learning with incomplete data
Skills you'll gain
This course includes:
5.45 Hours PreRecorded video
8 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 8 modules in this course
This advanced course focuses on learning probabilistic graphical models (PGMs) from data. Students explore parameter estimation in directed and undirected models, structure learning for directed models, and handling incomplete data. The curriculum covers maximum likelihood estimation, Bayesian estimation, Markov networks, and the Expectation Maximization (EM) algorithm. Through programming assignments, students implement key learning algorithms and apply them to real-world problems.
Learning: Overview
Module 1 · 15 Minutes to complete
Review of Machine Learning Concepts
Module 2 · 58 Minutes to complete
Parameter Estimation in Bayesian Networks
Module 3 · 2 Hours to complete
Learning Undirected Models
Module 4 · 21 Hours to complete
Learning BN Structure
Module 5 · 17 Hours to complete
Learning BNs with Incomplete Data
Module 6 · 22 Hours to complete
Learning Summary and Final
Module 7 · 50 Minutes to complete
PGM Wrapup
Module 8 · 24 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
298 ratings
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