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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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51 - 56 of 56 Reviews for Probabilistic Graphical Models 2: Inference

By fan

Nov 20, 2016

Can't get score for free!!!

By Phillip W

May 01, 2019

I enjoyed learning about this exciting field. Though, the explanations need some more examples to generalize. Also, I found that there is a big gap between the videos and the programming assignments. Either the programming assignments get more theoretical explanations, maybe with some examples too, or the videos get more applied than they are now.

By Hunter J

May 02, 2017

The lectures are fine and the book is great, but the assignments have a lot of technical problems. I spent most of my effort trying to solve trivial issues with the sample code and dealing with the auto grader.

By Jiaxing L

Nov 27, 2016

I am kind of disappointed that you have to pay for the course before you can submit the solution to the problem set. However, that is not the main issue of this course, as I fully understand that the financial profit for the lecturer is very important. The main issue of this course is that the chaos in the symbol used in the second programming assignment, the lecturer cannot even main self-consistency in the symbol used. The statement of everything in both PA1 and PA2 is also very confusing.

By Mahmoud S

Feb 22, 2019

The honorary assignments contain code mistakes, and difficult to do! You are sifting through mistakes in the instructions along with the supplemented code!

By Deleted A

Nov 18, 2018

This course seems to have been abandoned by Coursera. Mentors never reply to discussion forum posts (if there is any active mentor at all). Many assignments and tests are confusing and misleading. There are numerous materials you can find online to learn about Graphical Models than spending time & money on this.