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Learner Reviews & Feedback for Probabilistic Graphical Models 1: Representation by Stanford University

4.7
1,146 ratings
247 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 first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly....

Top reviews

ST

Jul 13, 2017

Prof. Koller did a great job communicating difficult material in an accessible manner. Thanks to her for starting Coursera and offering this advanced course so that we can all learn...Kudos!!

CM

Oct 23, 2017

The course was deep, and well-taught. This is not a spoon-feeding course like some others. The only downside were some "mechanical" problems (e.g. code submission didn't work for me).

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101 - 125 of 243 Reviews for Probabilistic Graphical Models 1: Representation

By Fabio S

Sep 25, 2017

Excellent, well structured, clear and concise

By Gautam K

Oct 17, 2016

This course probably the only best of class course available online. Prof Daphne Koller is one of the very few authority on this subject. I am glad to sign up this course and after completing gave me a great satisfaction learning Graphical Model. I also purchased the book written by Prof. Koller and Prof Friedman and I am going to continue my study on this subject.

By Al F

Mar 20, 2018

Excellent Course. Very Deep Material. I purchased the Text Book to allow for a deeper understanding and it made the course so much easier. Highly recommended

By Johannes C

Mar 08, 2018

necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.

By Abhishek K

Nov 13, 2016

Superb exposition. Makes me want to continue learning till the very end of this course. Very intuitive explanations. Plan to complete all courses offered in this specialization.

By Nguyễn L T Â

Feb 06, 2018

Thank you, the professor.

By oilover

Dec 03, 2016

老师很棒!!

By Venkateshwaralu

Oct 26, 2016

I loved every minute of this course. I believe I can now understand those gory details of representing an algorithm and comfortably take on challenges that require construction and representation of a functional domain. On a different note, nurtured a new found respect for the graph data structure!

By albert b

Nov 04, 2017

Best course anywhere on this topic. Plus Daphne is the best !

By 吕野

Dec 26, 2016

Good course lectures and programming assignments

By Souvik C

Oct 26, 2016

Extremely helpful course

By Musalula S

Aug 02, 2018

Great course

By José A R

Sep 14, 2018

Excellent course. Very well explained with precise detail and practical material to consolidate knowledge.

This was my first approach to PGM and end it fascinated. Will look to learn more from this subject.

Thank you very much Daphne!!

By ALBERTO O A

Oct 16, 2018

Really well structured course. The contents are complemented with the book. It is a time consuming course. Totally enjoyed!

By Umais Z

Aug 23, 2018

Brilliant. Optional Honours content was more challenging than I expected, but in a good way.

By M A B

Aug 31, 2018

Excellent course, the effort of the instructor is well reflected in the content and the exercices. A must for every serious student on (decision theory or markov random fields tasks.

By BOnur b

Nov 13, 2018

Great course. Recommended to everyone who have interest on bayesian networks and markov models.

By Alexandru I

Nov 25, 2018

Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.

By 张浩悦

Nov 22, 2018

funny!!

By PRABAL B D

Sep 01, 2018

Awesome Course. I got to learn a lot of useful concepts. Thank You.

By Renjith K A

Sep 23, 2018

Was really helpful in understanding graphic models

By Yue S

May 09, 2019

Great course!

By Vivek G

Apr 27, 2019

Great course. some programming assignments are tough (not too nicely worded and automatic grader can be a bit annoying) but all in all, great course

By 郭玮

Apr 26, 2019

Really nice course, thank you!

By Chahat C

May 04, 2019

lectures not good(i mean not detailed)