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 hy395

•Sep 13, 2017

Very clear and intuitive.

par Labmem

•Oct 03, 2016

Great Course!!!!!

par Justin C

•Oct 23, 2016

This was a fantastic introduction to PGM for a non-expert. It is well paced for an online course and the assignments provide enough depth to hone your knowledge and skills within the 5 week timeframe. Highly recommended.

par Dawood A C

•Oct 25, 2016

The course was very fruitful. It is was not that easy of course, I think it is one of the most difficult courses on Coursera but it deserves to try it once, twice and as many as you can until you understand the idea behind the course. The exams and the honor assignments were so tricky and not that easy to solve. If you don't have a probabilistic background, I think first better for you to take a course in data analysis and probability.

par Jinsun P

•Jan 17, 2017

Really Helpful for Studying!

par Arthur C

•Jun 04, 2017

Super useful if you want to understand any probability model.

par Sriram P

•Jun 24, 2017

Had a wonderful learning experience, Thank You Daphne Ma'am.

par Isaac A

•Mar 23, 2017

A great introduction to Bayesian and Markov networks. Challenging but rewarding.

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 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 Siyeong L

•Jan 22, 2017

Awesome!!!

par Kelvin L

•Aug 11, 2017

I guess this is probably the most challenging one in the Coursera. Really Hard but really rewarding course!

par Yang P

•Apr 26, 2017

Great course.

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