Jul 12, 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 22, 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).
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 Diogo P•
Oct 11, 2017
Great course. The lectures are rather clear and the assignments are very insightful. It takes some time to complete, mostly if you are interested in doing the Honor programming assignments (and you really should be, because these are demanding but also very useful). Previous knowledge on basic probability theory and machine learning is highly recommended.
By SIYI Y•
Nov 3, 2016
This is definitely a good course. The honors assignments are interesting, which instruct you to implement graphical models from scratch to solve problems in real world using Matlab or Octave. This helps me understand the theory part better and allows me to have better sense how they can work practically applications.
By Siwei G•
Jun 7, 2017
It's a great class. A lot people may complain that there should be more details. Well, this course may not hold your hands all the way to the end, but it covers enough to get you started to learn independently. It is a graduate level class, and it should be designed in this way. 5 star for the wonderful content.
By Jaewoo S•
Apr 26, 2020
Wow, it was a hard course. And it is usually true for hard courses, I really learned a lot. I truly recommend to solve all honors contents to get thorough understanding. Meanwhile, some programming assignment contents need to be either fixed or improved. There have been many discussions in the discussion forum.
By Eric S•
Feb 1, 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.
By Douglas G•
Oct 24, 2016
This course is very help for who have to study anything the respect of machine learning example, which is a thing much used in every day and in the new context of new industries 4.0, and the studies of probabilistcs graphical can help who need to develop new programs each times more efectiviness and best.
By Venkateshwaralu S•
Oct 25, 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 Ryan D•
Jun 21, 2017
Quiz and Video Lecture content was good. Would have preferred different format for programming assignments. The 30 minute life time of programming assignment submission tokens was pretty inconvenient. Overall great course. Definitely more challenging than the Machine Learning course material.
By Jorge P•
Feb 26, 2017
Brilliant course, extremely challenging. Prof. Koller does a great job explaining the concepts and uses up-to-date and useful examples. The quizzes are the hardest I've faced in Coursera, this course is no joke, it will take time, effort and taking notes to get through it.
By Juan P J A•
May 11, 2022
Great content, explanations and flow . Explanations are dense sometimes so it requires to stop or replay the video. I gave up on the extra material given the problems with the automatic online submission (this should be updated). In general, I highly recommend it!
By roi s•
Oct 29, 2017
I really like how Dafna is teaching the course, very clear!
It will be nice if their could be a following course that will show new frameworks and code that implements PGMs. Like the courses of deep learning where Andrew Ng is focusing mostly on the practical side.
By Anurag P•
Jan 8, 2018
The course is quite hard, however it becomes easier if you follow the book along with course. Also, programming assignments need to improved, the bugs and known issues mentioned in forum should be incorporated to prevent people from wasting time on setup issues.
By Yuxuan X•
Aug 7, 2017
Awsome course for Information/Knowledge Engineering. Although not necessary to finish all the honor assignments, it is highly recommended to implement them. Not only for comprehension, but also practice. You can actually apply them on your career or research.
By Minh N•
Mar 1, 2017
Quite a steep learning curve. Definitely not for those without prior experience in machine learning, or statistics in general. Also, I would much appreciate it if more test cases were provided in the programming assignments to help with debugging.
By Christophe K•
Oct 22, 2016
Very challenging course, but hey, if you are here, you are looking for that!
Lots of knowledge to absorb, but that leads you to a deep understanding on Probability Graphs properties.
I've learnt a lot and I really enjoyed taking this course.
By Maxim V•
Apr 29, 2020
Basic but absolutely necessary knowledge (representation). Quizzes were surprisingly easy. The best (and in my opinion absolutely necessary) part are the honor assignments, they make the course not just a little but many times better.
By José A R•
Sep 13, 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 Chatard J•
Nov 25, 2016
Une méthode pédagogique sans faille. Des contrôles et des exercices qui permettent d'approfondir ce qu'on apprend et de faire le point en permanence. Un merveilleux voyage dans le monde des Modèles Graphiques Probabilistes.
By Justin C•
Oct 23, 2016
This was a fantastic introduction to PGM for a non-expert. It is well paced for an online course and the assignments provide enough depth to hone your knowledge and skills within the 5 week timeframe. Highly recommended.
By KE Z•
Nov 23, 2017
All Programming Assignments are challenging (Bayesian net, Markov net/CRF, and decision making), but very essential to help understand how PGM works. I definitely will enroll the second course in this specialization.
By Alexey K•
Nov 17, 2017
Thank you! It's simply incredible exercise for brain! :-) The best ever course here, which teaches one to really think and model, rather than merely click to choose most plausible answer ( like other courses do )
By Supakorn S•
Apr 27, 2022
The instructor provide clear explanations and useful examples.
Reading the recomended resourses, including the books, are also help me to comprehend the course contents.
Great course overall, thanks
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 Jorge C•
Sep 17, 2017
Sugerencia: Algunos de los ejemplos numéricos presentados en el curso podrían ir acompañados de alguna expresión matemática intermedia que facilite la comprensión de los mismos.