Probabilistic Graphical Models

Completed by KE ZHU

June 16, 2018

Approximately 4 months at 10 hours a week to complete

Course Certificates Completed

Probabilistic Graphical Models 1: Representation

Probabilistic Graphical Models 2: Inference

Probabilistic Graphical Models 3: Learning

Probabilistic Graphical Models

Stanford University

4.6 (1,280 ratings)

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27K Students Enrolled

View certificate for KE ZHU, Probabilistic Graphical Models , offered through Coursera. 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. SOME ONLINE COURSES MAY DRAW ON MATERIAL FROM COURSES TAUGHT ON-CAMPUS BUT THEY ARE NOT EQUIVALENT TO ON-CAMPUS COURSES. THIS STATEMENT DOES NOT AFFIRM THAT THIS PARTICIPANT WAS ENROLLED AS A STUDENT AT STANFORD UNIVERSITY IN ANY WAY. IT DOES NOT CONFER A STANFORD UNIVERSITY GRADE, COURSE CREDIT OR DEGREE, AND IT DOES NOT VERIFY THE IDENTITY OF THE PARTICIPANT.

Course Certificates

Earned after completing each course in the Specialization

Probabilistic Graphical Models 1: Representation

Stanford University

Taught by: Daphne Koller

Completed by: KE ZHU by November 23, 2017

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Probabilistic Graphical Models 2: Inference

Stanford University

Taught by: Daphne Koller

Completed by: KE ZHU by December 16, 2017

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Probabilistic Graphical Models 3: Learning

Stanford University

Taught by: Daphne Koller

Completed by: KE ZHU by June 16, 2018

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