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

4.7
1,128 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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126 - 150 sur 239 Examens pour Probabilistic Graphical Models 1: Representation

par Hang D

Oct 09, 2016

really well taught

par yuri f

May 15, 2017

great course

par Anil K

Oct 30, 2017

Very intuitive...

par Matt M

Oct 22, 2016

Very interesting and challenging course. Now hoping to apply some of the techniques to my Data Science work.

par Gautam B

Jul 04, 2017

Great course loved the ongoing feedback when doing the quizes.

par 艾萨克

Nov 07, 2016

useful! A little diffcult

par Siwei G

Jun 07, 2017

It's a great class. A lot people may complain that there should be more details. Well, this course may not hold your hands all the way to the end, but it covers enough to get you started to learn independently. It is a graduate level class, and it should be designed in this way. 5 star for the wonderful content.

par chen h

Jan 21, 2018

The exercise is a little difficult. Need to revise several times to fully digest.

par Ning L

Oct 18, 2016

This is a very good course for the foundation knowledge for AI related technologies.

par Amritesh T

Nov 25, 2016

highly recommended if you wanna learn the basics of ML before getting into it.

par Jonathan H

Nov 25, 2017

This course is hard and very interesting!

par Miriam F

Aug 27, 2017

Very nice and well prepared course!

par Lilli B

Feb 02, 2018

Brilliant content and charismatic lecturer!!!

par Ryan D

Jun 21, 2017

Quiz and Video Lecture content was good. Would have preferred different format for programming assignments. The 30 minute life time of programming assignment submission tokens was pretty inconvenient. Overall great course. Definitely more challenging than the Machine Learning course material.

par Jiew W

Apr 17, 2018

very good, practical.

par Chan-Se-Yeun

Jan 07, 2018

This course is quite interesting not that easy. It helps me understand Markov network. The questions within the video are very helpful. It helps me check out some essential concepts and details. What's more, I'm fascinated by the teacher's voice and her teaching style, though detailed reading is required off class to gain comprehensive understanding. This is the first time I take online course in courser, and it's fun. I think I'll keep on learning the rest 2 courses of this series.

par Sureerat R

Mar 02, 2018

This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.

par Diogo P

Oct 11, 2017

Great course. The lectures are rather clear and the assignments are very insightful. It takes some time to complete, mostly if you are interested in doing the Honor programming assignments (and you really should be, because these are demanding but also very useful). Previous knowledge on basic probability theory and machine learning is highly recommended.

par Yuxuan X

Aug 08, 2017

Awsome course for Information/Knowledge Engineering. Although not necessary to finish all the honor assignments, it is highly recommended to implement them. Not only for comprehension, but also practice. You can actually apply them on your career or research.

par KE Z

Nov 23, 2017

All Programming Assignments are challenging (Bayesian net, Markov net/CRF, and decision making), but very essential to help understand how PGM works. I definitely will enroll the second course in this specialization.

par roi s

Oct 29, 2017

I really like how Dafna is teaching the course, very clear!

It will be nice if their could be a following course that will show new frameworks and code that implements PGMs. Like the courses of deep learning where Andrew Ng is focusing mostly on the practical side.

par Jax

Jan 09, 2017

very nice

par Jorge C

Sep 17, 2017

Sugerencia: Algunos de los ejemplos numéricos presentados en el curso podrían ir acompañados de alguna expresión matemática intermedia que facilite la comprensión de los mismos.

par Chahat C

May 04, 2019

lectures not good(i mean not detailed)

par Sumod K M

May 06, 2019

The course contents and presentation is of very high quality. The assignments and quizzes are both challenging and very rewarding. The only minor qualm is that the programming assignment grader seems to have few issues. For one, MATLAB indexing is really hard to work with. Secondly, it doesn't test the answers fully in some cases. Like the case of OptimizeWithJointUtility, OptimizeLinearExpectations. My codes passed the grader but I was splitting to hair to figure out why my answers to quiz questions corresponding to programming assignment were wrong. Turned out that my code was incorrect for the two programming assignments and that was causing issues. Otherwise, really nice course. Thank you :).