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!

Very good and excellent course and assignment

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.

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

Great course !

good online coursera

great course

very good!

Very informative course, and incredibly useful in research

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

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

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

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

One of the best courses which i visited.

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

excellent explanations! Thanks professor!

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

Excellent material presentation

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!

Great content!

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

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

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.

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.

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.

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.