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Learner Reviews & Feedback for Probabilistic Graphical Models 2: Inference by Stanford University

4.6
stars
478 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 problems. This course is the second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem....

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.

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26 - 50 of 74 Reviews for Probabilistic Graphical Models 2: Inference

By Julio C A D L

Apr 9, 2018

I would have like to complete the honors assignments, unfortunately, I'm not fluent in Matlab. Otherwise, great course!

By kat i

Dec 7, 2020

Amazing course offering a technical as well as intuitional understanding of the principles of doing inference

By Evgeniy Z

Mar 10, 2018

Very interesting course. However, even after completing it with honors, I feel like I don't understand a lot.

By HARDIAN L

May 19, 2020

Great balance between theories and practices. Also provide a lot of intuitions to understand the concepts

By Una S

Sep 2, 2020

Amazing course! Loved how Daphne explained very complicated things in an understandable manner!

By Martin P

Jan 20, 2021

Great course! Course has filled gaps in my knowledge from statistics and similar sciences.

By Ruiliang L

Feb 24, 2021

Awesome class to gain better understanding of inference for graphical model

By Sriram P

Jun 24, 2017

Had a wonderful and enriching fun filled experience, Thank you Daphne Ma'am

By Jerry R

Dec 22, 2017

Great course! Expect to spend significant time reviewing the material.

By Anil K

Nov 5, 2017

This course induces lateral thinking and deep reasoning.

By Liu Y

Mar 18, 2018

Really a interesting, challenging and great course!

By KE Z

Dec 29, 2017

Very valuable course! I am glad I made it.

By Tim R

Oct 4, 2017

Very interesting, more advanced material

By Arthur C

Jul 19, 2017

Difficult, but it makes you think a lot!

By Dat Q D

Jan 26, 2022

the content is very hard

By chen h

Feb 5, 2018

Interest but difficult.

By Ram G

Sep 14, 2017

Great job Prof. Koller!

By Musalula S

Aug 2, 2018

This is a great course

By Wei C

Mar 6, 2018

good way to learn PGM,

By Alexander K

Jun 3, 2017

Thank You for all.

By Wenjun W

May 21, 2017

Awesome class!

By 郭玮

Nov 12, 2019

Very helpful.

By Anderson R L

Nov 3, 2017

Great course!

By Alireza N

Jan 12, 2017

Excellent!

By hanbt

Jun 8, 2018

Very good