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 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 Chuck M

•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).

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 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 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 Christopher M P

•Jan 16, 2020

Simply excellent. A wonderful course to begin the representation of PGM. Be advised.... this can get quite advanced. It's all about that Bayes, 'bout that Bayes.... no trouble.

創建者 Christopher B

•Jul 17, 2017

learned a lot. lectures were easy to follow and the textbook was able to more fully explain things when I needed it. looking forward to the next course in the series.

創建者 Anthony L

•Jul 20, 2019

Some parts are challenging enough in the PAs, if you are familiar with Matlab this course is a great opportunity to get familiar with PGMs and learn to handle these.

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 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 Vivek G

•Apr 27, 2019

Great course. some programming assignments are tough (not too nicely worded and automatic grader can be a bit annoying) but all in all, great course

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 Angel G G

•Dec 12, 2019

Great course, I miss some programming assignments (I didn't do the "honors"), but the quizzes are already good to test your general understanding.

創建者 Ayush T

•Aug 23, 2019

This course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.

創建者 Valeriy Z

•Nov 14, 2017

This course gives a solid basis for the understanding of PGMs. Don't take it too fast. It takes some time to get used to all the concepts.

創建者 Isaiah O M

•Mar 31, 2019

I found well structured contend of these rare probabilistic methods (Actually this is the only reasonable course in this approach online)

創建者 Saikat M

•Aug 01, 2017

Not as rigorous as the book, but very good. However, Octave should not be be necessary and is a road block to completing assignments.

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 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 Mike P

•Jul 30, 2019

An excellent course, Daphne is one of the top people to be teaching this topic and does an excellent job in presentation.

創建者 Matt M

•Oct 22, 2016

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

創建者 Anton K

•May 07, 2018

This was my first experience with Coursera! Thanks prof. Daphne Koller for this course and Coursera at all.

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!

創建者 杨涛

•Mar 27, 2019

I think this course is quite useful for my own research, thanks Cousera for providing such a great course.

創建者 HARDIAN L

•Jun 23, 2018

Even though this is the most difficult course I have ever taken in Coursera, I really enjoyed the process.