Back to Probabilistic Graphical Models 2: Inference
Learner Reviews & Feedback for Probabilistic Graphical Models 2: Inference by Stanford University
488 ratings
About the Course
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 proble...
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 proble...
Top reviews
AT
Aug 22, 2019
Just like the first course of the specialization, this course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.
AL
Aug 19, 2019
I have clearly learnt a lot during this course. Even though some things should be updated and maybe completed, I would definitely recommend it to anyone whose interest lies in PGMs.
Filter by:
76 - 78 of 78 Reviews for Probabilistic Graphical Models 2: Inference
By Tomer N
•Jun 20, 2018
The Programming assignment must be updated and become relevant... They are way too hard and not friendly...
By Thomas W
•May 5, 2017
Great but it would be nice to have some introduction to approximate inference methods as well.
By fan
•Nov 19, 2016
Can't get score for free!!!