Probabilistic Graphical Models 2: Inference
Master advanced inference algorithms for probabilistic graphical models, from exact methods to sampling approaches.
Master advanced inference algorithms for probabilistic graphical models, from exact methods to sampling approaches.
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.
4.6
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English
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What you'll learn
Execute variable elimination and message passing algorithms
Analyze graph structure impact on inference complexity
Implement MCMC algorithms including Gibbs sampling
Design effective Metropolis Hastings proposals
Skills you'll gain
This course includes:
7.2 Hours PreRecorded video
8 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 7 modules in this course
This advanced course explores probabilistic inference in graphical models, covering both exact and approximate algorithms. Students learn variable elimination, belief propagation, MAP inference, and sampling methods including Markov Chain Monte Carlo (MCMC) and Gibbs sampling. The curriculum emphasizes practical implementation through programming assignments and real-world applications in areas such as medical diagnosis, image understanding, and speech recognition.
Inference Overview
Module 1 · 25 Minutes to complete
Variable Elimination
Module 2 · 1 Hours to complete
Belief Propagation Algorithms
Module 3 · 18 Hours to complete
MAP Algorithms
Module 4 · 1 Hours to complete
Sampling Methods
Module 5 · 14 Hours to complete
Inference in Temporal Models
Module 6 · 49 Minutes to complete
Inference Summary
Module 7 · 42 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
483 ratings
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