Probabilistic Graphical Models 1: Representation(으)로 돌아가기

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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).

필터링 기준:

교육 기관: Nguyễn L T Â

•Feb 06, 2018

Thank you, the professor.

교육 기관: oilover

•Dec 03, 2016

老师很棒！！

교육 기관: Souvik C

•Oct 26, 2016

Extremely helpful course

교육 기관: Naveen M N S

•Dec 13, 2016

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

교육 기관: Logé F

•Nov 19, 2017

Great course !

교육 기관: Wei C

•Mar 06, 2018

good online coursera

교육 기관: liang c

•Nov 15, 2016

Great course. and it is really a good chance to study it well under Koller's instruction.

교육 기관: 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.

교육 기관: Jose A A S

•Nov 25, 2016

Wonderful

교육 기관: Kar T Q

•Mar 02, 2017

Excellent course.

교육 기관: Dimitrios K

•Oct 31, 2016

So happy to complete this one. It was tough - especially the programming exercises and mainly due to high degree of vague-ness and un-expressiveness of matlab/octave in contrast to e.g. Python or Scala. samiam was unexpectedly handy and usable. Very nice and educational piece of software. Excellent course - it's incredible how many Machine Learning models are expressed under the umbrella of PGMs.

교육 기관: SIVARAMAKRISHNAN V

•Jan 06, 2017

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

교육 기관: Arjun V

•Dec 04, 2016

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

교육 기관: 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.

교육 기관: David D

•May 30, 2017

Mind blowing!

교육 기관: Anton K

•May 07, 2018

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

교육 기관: Alejandro D P

•Jun 30, 2018

This and its sequels, the most interesting Coursera courses I've taken so far.

교육 기관: Youwei Z

•May 20, 2018

Very informative. The only drawback is lack of rigorous proof and clear definition summaries.

교육 기관: 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.

교육 기관: Roger T

•Mar 05, 2017

very challenging class but very rewarding as well!

교육 기관: 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!!

교육 기관: Alexander A S G

•Feb 10, 2017

Thanks

교육 기관: Alexander K

•May 16, 2017

Thank you for all. This is gift for us.

교육 기관: mohammed o

•Oct 18, 2016

Fantastic

교육 기관: 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!)...