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

4.6
370 ratings
56 reviews

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 23, 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 20, 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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1 - 25 of 56 Reviews for Probabilistic Graphical Models 2: Inference

By Shi Y

Dec 16, 2018

It's absolutely very very hard but extremely interesting course! Although code assignments always have a lot of small bugs, and it cost me lots of time to find out, but, hey! Everything is the same in school(offline), nothing gonna be perfect. The sampling part is the most difficult stuff to learn so far, and after I tried to review it again and again, combined with other online material, I got those shit done! The only drawback of this course is that not many people active in the forum(Including those TA), maybe that just because only a small number of people enrolled in this course. In short, worth learning!

By Anurag S

Nov 08, 2017

Great introduction to inference. Requires some extra reading from the textbook.

By Jonathan H

Aug 04, 2017

Pretty good course, albeit very dense compared to the first one (which was certainly not trivial). I would give it 5 stars just based on the content, but the programming assignments don't work without significant extra effort. I completed the honors track for the first course, but gave up after spending 4 hours trying to fix HW bugs that were reported 8 months ago.

Would have also been nice to have more practical examples to work on. Some of the material is very theoretical, and I find it hard to build intuitions without applying the algorithms in practice.

By george v

Nov 28, 2017

great course, though really advanced. would like a bit more examples especially regarding the coding. worth it overally

By Kaixuan Z

Dec 05, 2018

hope to get some feedbacks about hw or exam

By Tianyi X

Feb 23, 2018

not very clear from the top-down level.

By Michel S

Jul 14, 2018

Good course, but the material really needs a refresh!

By Lik M C

Feb 03, 2019

Very great course! A lot of things have been learnt. The lectures, quiz and assignments clear up all key concepts. Especially, assignments are wonderful!

By Sriram P

Jun 24, 2017

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

By KE Z

Dec 29, 2017

Very valuable course! I am glad I made it.

By hanbt

Jun 08, 2018

Very good

By Alexander K

Jun 03, 2017

Thank You for all.

By Chan-Se-Yeun

Jan 31, 2018

I kind of like the teacher. She can always explain complicated things in a simple way, though the notes she writes in the slides are all in free style. Loopy belief propagation and dual decomposition are the best things I've learnt in this course. I've met them before in some papers, but I found it extremely hard to understand then. Now I gain some significant intuition of them and I'm ready to do further exploration. Anyway, I'll keep on learning course 3 to achieve my first little goal in courser.

By Alireza N

Jan 12, 2017

Excellent!

By Simon T

Sep 14, 2017

Great job Prof. Koller!

By Jerry A R

Dec 22, 2017

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

By 王文君

May 21, 2017

Awesome class!

By Péter D

Nov 14, 2017

awesome

By Anderson d R L

Nov 03, 2017

Great course!

By Julio C A D L

Apr 09, 2018

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

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 Tim R

Oct 04, 2017

Very interesting, more advanced material

By Arthur C

Jul 19, 2017

Difficult, but it makes you think a lot!

By llv23

Mar 12, 2017

Thanks a lot for professor D.K.'s great course for PGM inference part. Really a very good starting point for PGM model and preparation for learning part.

By Wei C

Mar 06, 2018

good way to learn PGM,