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 SIVARAMAKRISHNAN V

•Jan 06, 2017

Great course. Thanks Daphne Koller, this is really motivating :)

par Arjun V

•Dec 04, 2016

A great course, a must for those in the machine learning domain.

par Ka L K

•Mar 27, 2017

A five stars course. Prof. Koller is an outstanding scientists in this field. The first part just introduce you two basic frames of graphical models. So go further into second part is necessary if you want to have a bigger picture. The whole course is an introduction to the book - Probabilistic Graphical Models of Prof. Koller, so buying her book is also highly recommended. This course is supposed to be hard, so you should expect a steep learning curve. But all the efforts you made are worthy. I suggest coursera will consider put more challenging exercises in order to extent the concentration. Finally, a highly respect to Prof. Koller who provide the course in such a theoretical depth.

par David D

•May 30, 2017

Mind blowing!

par Anton K

•May 07, 2018

This was my first experience with Coursera! Thanks prof. Daphne Koller for this course and Coursera at all.

par Valeriy Z

•Nov 14, 2017

This course gives a solid basis for the understanding of PGMs. Don't take it too fast. It takes some time to get used to all the concepts.

par Roger T

•Mar 05, 2017

very challenging class but very rewarding as well!

par Simon T

•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!!

par Alexander A S G

•Feb 10, 2017

Thanks

par Alexander K

•May 16, 2017

Thank you for all. This is gift for us.

par mohammed o

•Oct 18, 2016

Fantastic

par Rishi C

•Jan 29, 2018

Perhaps the best introduction to AI/ML - especially for those who think "the future ain't what it used to be"; the mathematical techniques covered by the course form a toolkit which can be easily thought of as "core", i.e. a locus of strength which enables a wide universe of thinking about complex problems (many of which were correctly not thought to be tractable in practice until very recently!)...

par 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.

par Blake B

•May 21, 2017

Awesome intro to graphical models, and the exercises really emphasize understanding and proceed at what seems like the appropriate pace. Challenging for sure, you need to want to learn this stuff. Only downside is I'm not a fan of using octave/matlab--really wish this could be rebuilt using python for all the exercises. I've probably spent 60% of my time devoted to this course on getting that setup working and wrestling with telling the computer to do what I want in an unpopular language--at least, unpopular out in the world outside of academia.

par Sha L

•Apr 20, 2017

it's really hard course for me but after completing and see the certificate I feel so good about it. Yesterday someone asked a question regarding conditional independence. I remember before I took the course I've spent quite some time understanding it, just like him. But yesterday I didn't event think about it and gave him the right answer using "active trail" and "D-separation" concept. That's how powerful this course can be.

I didn't work on the honor track though because I'm currently short of time. But I think I will come back and taking the other 2 courses in this series.

par 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!

par llv23

•Jul 19, 2017

Very good and excellent course and assignment

par 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.

par Phan T B

•Dec 02, 2016

very good!

par Ziheng

•Nov 14, 2016

Very informative course, and incredibly useful in research

par Elvis S

•Oct 29, 2016

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

par Hao G

•Nov 01, 2016

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

par 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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par Shengliang

•May 29, 2017

excellent explanations! Thanks professor!

par George S

•Jun 18, 2017

Excellent material presentation

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