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 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 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 Johannes C

•Mar 08, 2018

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

創建者 Phan T B

•Dec 02, 2016

very good!

創建者 Ziheng

•Nov 14, 2016

Very informative course, and incredibly useful in research

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 Hao G

•Nov 01, 2016

Awesome course! I feel like bayesian method is also very useful for inference in daily life.

創建者 吕野

•Dec 26, 2016

Good course lectures and programming assignments

創建者 Ofelia P R P

•Dec 11, 2017

Curso muy completo que da conocimiento realmente avanzado sobre modelos gráficos probabilísticos. Aviso, la especialización es complicada para los que no somos expertos del tema!

創建者 王文君

•May 21, 2017

Awesome class, the content is not too easy as most online courses. Still the instructor states the concepts clearly and the assignments aligns very well with the content to help me deepen my understanding of the concepts. The assignments are meaningful and challenging, finishing them gave me a great sense of achievement!!

It would be better if the examples in the classes could incorporate some industry applications.

創建者 llv23

•Jul 19, 2017

Very good and excellent course and assignment

創建者 Souvik C

•Oct 26, 2016

Extremely helpful course

創建者 George S

•Jun 18, 2017

Excellent material presentation

創建者 Wei C

•Mar 06, 2018

good online coursera

創建者 Logé F

•Nov 19, 2017

Great course !

創建者 Elvis S

•Oct 29, 2016

Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.

創建者 Mohammd K D

•Apr 03, 2017

One of the best courses which i visited.

The explanation was so simple and there were many examples which were so helpful for me

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創建者 Shengliang

•May 29, 2017

excellent explanations! Thanks professor!

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 David C

•Nov 01, 2016

If you are interested in graphical models, you should take this course.

創建者 Chatard J

•Nov 25, 2016

Une méthode pédagogique sans faille. Des contrôles et des exercices qui permettent d'approfondir ce qu'on apprend et de faire le point en permanence. Un merveilleux voyage dans le monde des Modèles Graphiques Probabilistes.

創建者 Siyeong L

•Jan 22, 2017

Awesome!!!

創建者 Kelvin L

•Aug 11, 2017

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