Back to Probabilistic Graphical Models 2: Inference

4.6

377 ratings

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56 reviews

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....

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.

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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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 george v

•Nov 28, 2017

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

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 Kaixuan Z

•Dec 05, 2018

hope to get some feedbacks about hw or exam

By Michel S

•Jul 14, 2018

Good course, but the material really needs a refresh!

By Tianyi X

•Feb 23, 2018

not very clear from the top-down level.

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 Musalula S

•Aug 02, 2018

This is a great course

By Alireza N

•Jan 12, 2017

Excellent!

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 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 Arthur C

•Jul 19, 2017

Difficult, but it makes you think a lot!

By chen h

•Feb 06, 2018

Interest but difficult.

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 Tim R

•Oct 04, 2017

Very interesting, more advanced material

By Rishi C

•Oct 28, 2017

Perhaps the best introduction to AI/ML - especially for those who think "the future ain't what it used to be"; the mathematical techniques covered by the course form a toolkit which can be easily thought of as "core", i.e. a locus of strength which enables a wide universe of thinking about complex problems (many of which were correctly not thought to be tractable in practice until very recently!)...

By Alexander K

•Jun 03, 2017

Thank You for all.

By Wei C

•Mar 06, 2018

good way to learn PGM,

By Liu Y

•Mar 18, 2018

Really a interesting, challenging and great course!

By Sriram P

•Jun 24, 2017

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

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 Simon T

•Sep 14, 2017

Great job Prof. Koller!

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