Back to Probabilistic Graphical Models 1: Representation

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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....

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!!

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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By Ofelia P R P

•Dec 11, 2017

Curso muy completo que da conocimiento realmente avanzado sobre modelos gráficos probabilísticos. Aviso, la especialización es complicada para los que no somos expertos del tema!

By llv23

•Jul 19, 2017

Very good and excellent course and assignment

By 王文君

•May 21, 2017

Awesome class, the content is not too easy as most online courses. Still the instructor states the concepts clearly and the assignments aligns very well with the content to help me deepen my understanding of the concepts. The assignments are meaningful and challenging, finishing them gave me a great sense of achievement!!

It would be better if the examples in the classes could incorporate some industry applications.

By Naveen M N S

•Dec 13, 2016

Basic course, but has few nuances. Very well instructed by Prof Daphne Koller.

By Logé F

•Nov 19, 2017

Great course !

By Wei C

•Mar 06, 2018

good online coursera

By Pedro R

•Nov 09, 2016

great course

By Phan T B

•Dec 02, 2016

very good!

By Ziheng

•Nov 14, 2016

Very informative course, and incredibly useful in research

By Elvis S

•Oct 29, 2016

Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.

By Stephen F

•Feb 26, 2017

This is a course for those interested in advancing probabilistic modeling and computation.

By Hao G

•Nov 01, 2016

Awesome course! I feel like bayesian method is also very useful for inference in daily life.

By David C

•Nov 01, 2016

If you are interested in graphical models, you should take this course.

By Mohammd K D

•Apr 03, 2017

One of the best courses which i visited.

The explanation was so simple and there were many examples which were so helpful for me

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By Shengliang

•May 29, 2017

excellent explanations! Thanks professor!

By Achen

•May 06, 2018

a bit too hard if you don't have enough probability knowledge

By George S

•Jun 18, 2017

Excellent material presentation

By Sergey V

•Oct 28, 2016

Done! The #PGM class is probably one of the most challenging ones in Coursera both in terms of workload and theoretical depth. I used to spend 10+ hours per week and I doubt anyone could complete it successfully without Matlab knowledge and strong background in #probability #machinelearning and #programming. Comprehensive programming assignment with honour content and quizzes help to make yourself very familar with the topics: #bayesiannetwork #gibbssampling #intercasualreasoning #markovproccess #markovchain #OCR Daphne Koller @DaphneKoller , as Coursera co-funder, made her best to show the capabilities of the platform. To sum up, prospective students should take into account that the course is quite advanced and several background in probability, statistics, machine learning and algorithms required if you going to sign up for the PGM class =) Lectures and videos available for free but graded assignments and verified certifcate is paid option. Cheers, @RiddleRus #stanford #math #probability #probabilisticmodels P.S. I had spent at least five attempts before I passed a final assignment!

By Diego T

•Jun 09, 2017

Great content!

By Rajmadhan E

•Aug 07, 2017

Awesome material. Could not get this experience by learning the subject ourselves using a textbook.

By Lucian B

•Jan 15, 2017

Some more exam questions and variation, including explanations when failing, would be very useful.

By ivan v

•Jul 31, 2017

Excellent introduction which covers a wide range of PGM related topics. I really liked programming assignments. They are not too difficult but extremely instructive.

Word of advice: although programming assignments are not mandatory, dare not to skip them. You will be missing an excellent learning experience.

Another useful advice: lectures are self-contained but reading the book helps a lot.

By Christopher B

•Jul 17, 2017

learned a lot. lectures were easy to follow and the textbook was able to more fully explain things when I needed it. looking forward to the next course in the series.

By Haowen C

•Sep 01, 2017

Excellent course for picking out just the critical portions of the Koller & Friedman book (which is over 1000 pages long, forget about reading it cover to cover for self study). Don't skip the programming assignments, they're very important for solidifying your understanding. You'll spend at least 75% of the time fussing over the somewhat arbitrary and baroque data structures used to represent factors and CPDs in this course, but at the end it's worth the frustration.

By Eric S

•Feb 01, 2018

A very in depth course on PGNs. You definitely need some background in math and a willingness to invest a lot of time into the course. Of most value to me were the programming exercises. They are in Octave as this is one of the earliest Coursera courses, but it is worth exploring the provided implementations.

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