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Avis et commentaires pour l'étudiant pour Probabilistic Graphical Models 1: Representation par Université de Stanford

4.7
1,126 notes
246 avis

À propos du cours

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....

Meilleurs avis

ST

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!!

CM

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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101 - 125 sur 239 Examens pour Probabilistic Graphical Models 1: Representation

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 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 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 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 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 )

par Chuck M

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).

par Phung H X

Oct 30, 2016

very good course

par liang c

Nov 15, 2016

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

par 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.

par Jose A A S

Nov 25, 2016

Wonderful

par Kar T Q

Mar 02, 2017

Excellent course.

par 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.

par SIVARAMAKRISHNAN V

Jan 06, 2017

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

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 Alejandro D P

Jun 30, 2018

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

par Youwei Z

May 20, 2018

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

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 Alireza N

Jan 12, 2017

Excellent!