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).
교육 기관: PRABAL B D•
Sep 01, 2018
Awesome Course. I got to learn a lot of useful concepts. Thank You.
교육 기관: Umais Z•
Aug 23, 2018
Brilliant. Optional Honours content was more challenging than I expected, but in a good way.
교육 기관: 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!
교육 기관: 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!!
교육 기관: Renjith K A•
Sep 23, 2018
Was really helpful in understanding graphic models
교육 기관: Ingyo C•
Oct 04, 2018
What a wonderful course that I haven't ever taken before.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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
교육 기관: Johannes C•
Mar 08, 2018
necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.
교육 기관: 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!
교육 기관: Diego T•
Jun 09, 2017
교육 기관: Rajmadhan E•
Aug 07, 2017
Awesome material. Could not get this experience by learning the subject ourselves using a textbook.
교육 기관: Lucian B•
Jan 15, 2017
Some more exam questions and variation, including explanations when failing, would be very useful.
교육 기관: 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.
교육 기관: Gary H•
Mar 28, 2018
Great instructor and information.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.
교육 기관: Prasid S•
Dec 08, 2016
Very well designed. There were areas here I struggled with the technical details and had to read up a lot to understand. The assignments are very well designed.
교육 기관: 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!
교육 기관: albert b•
Nov 04, 2017
Best course anywhere on this topic. Plus Daphne is the best !
교육 기관: 吕野•
Dec 26, 2016
Good course lectures and programming assignments
교육 기관: Abhishek K•
Nov 06, 2016
Difficult yet very good to understand even after knowing about ML for a long time.
교육 기관: Nguyễn L T Â•
Feb 06, 2018
Thank you, the professor.