Вернуться к 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....

автор: ST

•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!!

автор: CM

•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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Рецензии: 205

автор: Alexandru Iftimie

•Nov 25, 2018

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

автор: 张浩悦

•Nov 22, 2018

funny！！

автор: Larry Lyu

•Nov 18, 2018

This course seems to have been abandoned by Coursera. Mentors never reply to discussion forum posts (if there is any active mentor at all). Many assignments and tests are confusing and misleading. There are numerous materials you can find online to learn about Graphical Models than spending time & money on this.

автор: BOnur boyar

•Nov 13, 2018

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

автор: Shi Yihui

•Nov 13, 2018

总体上很棒的课程，除了第四周的荣誉编程的体验有待提升。课程难度适中，不容易，但认真思考和理解后是没有问题的。很期待专项课程中剩余的课程。

автор: Alain M

•Nov 03, 2018

Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.

автор: ALBERTO OLIVARES ALARCOS

•Oct 16, 2018

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

автор: Ingyo Chung

•Oct 04, 2018

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

автор: Renjith Kadeparambil Anil

•Sep 23, 2018

Was really helpful in understanding graphic models

автор: Sandeep Mavadia

•Sep 23, 2018

The content of the course is good but the assignments are in matlab which isn't as widely used as python and has the additional headache of licensing. it is the assignments where you really learn things so this is a serious negative point.

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