Retour à 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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par Pablo G M D

•Jul 18, 2018

Outstanding teaching and the assignments are quite useful!

par PRABAL B D

•Sep 01, 2018

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

par Umais Z

•Aug 23, 2018

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

par 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!

par 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!!

par Renjith K A

•Sep 23, 2018

Was really helpful in understanding graphic models

par Ingyo C

•Oct 04, 2018

What a wonderful course that I haven't ever taken before.

par 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.

par 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.

par 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

par Johannes C

•Mar 08, 2018

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

par 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!

par Diego T

•Jun 09, 2017

Great content!

par Rajmadhan E

•Aug 07, 2017

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

par Lucian B

•Jan 15, 2017

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

par 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.

par Gary H

•Mar 28, 2018

Great instructor and information.

par 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.

par 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.

par 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.

par 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.

par 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!

par albert b

•Nov 04, 2017

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

par 吕野

•Dec 26, 2016

Good course lectures and programming assignments

par Abhishek K

•Nov 06, 2016

Difficult yet very good to understand even after knowing about ML for a long time.

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