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Probabilistic Graphical Models 1: Representation, 斯坦福大学

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221 个审阅

课程信息

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....

热门审阅

创建者 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!!

创建者 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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214 个审阅

创建者 胡声鼎

Mar 10, 2019

A very nice-designed course

创建者 Mahmoud Shepero

Feb 25, 2019

Very good explanation and excellent assignments

创建者 Marno Basson

Feb 03, 2019

Absolutely love it!!!!

:)

创建者 Lorenzo Battarra

Jan 19, 2019

The course contents are presented very clearly. Difficult ideas are conveyed in a precise and convincing way. Despite this, the global structure is not presented very clearly, and the quality of some course material is not excellent. In particular, I didn't find the optional programming assignments particularly interesting, and the code/questions contained more than one bug. Also, the quality of video/sound is quite poor, and varies a lot from course to course.

创建者 Ben LI

Jan 13, 2019

Would be better if there are people monitoring the discussion board and actually answer student's questions.

创建者 Lik Ming Cheong

Jan 12, 2019

A great course! The provided training clarifies all key concepts

创建者 Utkarsh Agrawal

Dec 30, 2018

maza aa gaya

创建者 Myoungsu Choi

Dec 26, 2018

Writing on the ppt is not clear to see.

创建者 Xiaojie Zhang

Dec 22, 2018

Some interesting knowledges about PCM, but I think I need more detailed information in the succeeding courses.

创建者 Alexandru Iftimie

Nov 25, 2018

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