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).

篩選依據：

創建者 Fabio S

•Sep 25, 2017

Excellent, well structured, clear and concise

創建者 Gautam K

•Oct 17, 2016

This course probably the only best of class course available online. Prof Daphne Koller is one of the very few authority on this subject. I am glad to sign up this course and after completing gave me a great satisfaction learning Graphical Model. I also purchased the book written by Prof. Koller and Prof Friedman and I am going to continue my study on this subject.

創建者 Al F

•Mar 20, 2018

Excellent Course. Very Deep Material. I purchased the Text Book to allow for a deeper understanding and it made the course so much easier. Highly recommended

創建者 Johannes C

•Mar 08, 2018

necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.

創建者 Abhishek K

•Nov 13, 2016

Superb exposition. Makes me want to continue learning till the very end of this course. Very intuitive explanations. Plan to complete all courses offered in this specialization.

創建者 Gary H

•Mar 28, 2018

Great instructor and information.

創建者 Abhishek K

•Nov 06, 2016

Difficult yet very good to understand even after knowing about ML for a long time.

創建者 Nguyễn L T Â

•Feb 06, 2018

Thank you, the professor.

創建者 oilover

•Dec 03, 2016

老师很棒！！

創建者 Prasid S

•Dec 08, 2016

Very well designed. There were areas here I struggled with the technical details and had to read up a lot to understand. The assignments are very well designed.

創建者 Venkateshwaralu

•Oct 26, 2016

I loved every minute of this course. I believe I can now understand those gory details of representing an algorithm and comfortably take on challenges that require construction and representation of a functional domain. On a different note, nurtured a new found respect for the graph data structure!

創建者 albert b

•Nov 04, 2017

Best course anywhere on this topic. Plus Daphne is the best !

創建者 吕野

•Dec 26, 2016

Good course lectures and programming assignments

創建者 Souvik C

•Oct 26, 2016

Extremely helpful course

創建者 Musalula S

•Aug 02, 2018

Great course

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 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 Umais Z

•Aug 23, 2018

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

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 BOnur b

•Nov 13, 2018

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

創建者 Alexandru I

•Nov 25, 2018

Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.

創建者 张浩悦

•Nov 22, 2018

funny！！

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 Ingyo C

•Oct 04, 2018

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

創建者 Renjith K A

•Sep 23, 2018

Was really helpful in understanding graphic models