返回到 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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創建者 Jax

•Jan 09, 2017

very nice

創建者 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.

創建者 Isaac A

•Mar 23, 2017

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

創建者 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.

創建者 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.

創建者 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.

創建者 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.

創建者 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.

創建者 Jonathan H

•Nov 25, 2017

This course is hard and very interesting!

創建者 Amritesh T

•Nov 25, 2016

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

創建者 SIYI Y

•Nov 04, 2016

This is definitely a good course. The honors assignments are interesting, which instruct you to implement graphical models from scratch to solve problems in real world using Matlab or Octave. This helps me understand the theory part better and allows me to have better sense how they can work practically applications.

創建者 Anurag P

•Jan 08, 2018

The course is quite hard, however it becomes easier if you follow the book along with course. Also, programming assignments need to improved, the bugs and known issues mentioned in forum should be incorporated to prevent people from wasting time on setup issues.

創建者 Miriam F

•Aug 27, 2017

Very nice and well prepared course!

創建者 Lilli B

•Feb 02, 2018

Brilliant content and charismatic lecturer!!!

創建者 Renjith K A

•Sep 23, 2018

Was really helpful in understanding graphic models

創建者 José A R

•Sep 14, 2018

Excellent course. Very well explained with precise detail and practical material to consolidate knowledge.

This was my first approach to PGM and end it fascinated. Will look to learn more from this subject.

Thank you very much Daphne!!

創建者 PRABAL B D

•Sep 01, 2018

Awesome Course. I got to learn a lot of useful concepts. Thank You.

創建者 M A B

•Aug 31, 2018

Excellent course, the effort of the instructor is well reflected in the content and the exercices. A must for every serious student on (decision theory or markov random fields tasks.

創建者 ALBERTO O A

•Oct 16, 2018

Really well structured course. The contents are complemented with the book. It is a time consuming course. Totally enjoyed!

創建者 Ingyo C

•Oct 04, 2018

What a wonderful course that I haven't ever taken before.

創建者 BOnur b

•Nov 13, 2018

Great course. Recommended to everyone who have interest on bayesian networks and markov models.

創建者 张浩悦

•Nov 22, 2018

funny！！

創建者 Musalula S

•Aug 02, 2018

Great course

創建者 Pablo G M D

•Jul 18, 2018

Outstanding teaching and the assignments are quite useful!

創建者 Umais Z

•Aug 23, 2018

Brilliant. Optional Honours content was more challenging than I expected, but in a good way.