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Probabilistic Graphical Models 1: Representation に戻る

スタンフォード大学(Stanford University) による Probabilistic Graphical Models 1: Representation の受講者のレビューおよびフィードバック

4.6
1,347件の評価
300件のレビュー

コースについて

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....

人気のレビュー

ST
2017年7月12日

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!!

CM
2017年10月22日

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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Probabilistic Graphical Models 1: Representation: 76 - 100 / 293 レビュー

by Valeriy Z

2017年11月13日

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.

by Mulang' O

2019年3月31日

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

by Saikat M

2017年8月1日

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

by Karam D

2017年4月3日

One of the best courses which i visited.

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

by ALBERTO O A

2018年10月16日

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

by Mike P

2019年7月30日

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

by nipun

2021年5月29日

one of the best course I have ever followed. by all means it gave thorough understanding of every topic the introduced.

by Matt M

2016年10月22日

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

by Samuel d S B

2021年3月13日

Great course. Lectures gives us good intuition on definitions and results. Programming assignments are fun.

by Anton K

2018年5月7日

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

by Kelvin L

2017年8月11日

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

by 杨涛

2019年3月27日

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

by HARDIAN L

2018年6月23日

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

by satish p

2020年7月12日

A fantastic course and quite insightful. Require a strong grounding in probability theory to complete it.

by Johannes C

2020年4月19日

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

by Alexandru I

2018年11月25日

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

by Rajmadhan E

2017年8月7日

Awesome material. Could not get this experience by learning the subject ourselves using a textbook.

by Lucian

2017年1月15日

Some more exam questions and variation, including explanations when failing, would be very useful.

by Onur B

2018年11月13日

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

by Elvis S

2016年10月28日

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

by Youwei Z

2018年5月19日

Very informative. The only drawback is lack of rigorous proof and clear definition summaries.

by Umais Z

2018年8月23日

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

by Hao G

2016年11月1日

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

by Alfred D

2020年7月2日

Was a little difficult in the middle but the last section summary just refreshed all of it

by Stephen F

2017年2月26日

This is a course for those interested in advancing probabilistic modeling and computation.