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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
1,356件の評価
302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).

フィルター:

Probabilistic Graphical Models 1: Representation: 226 - 250 / 295 レビュー

by Boxiao M

2017年6月28日

The lecture was a bit too compact and unsystematic. However, if you also do a lot of reading of the textbook, you can learn a lot. Besides, the Quiz and Programming task are of high qualities.

by Shawn C

2016年11月5日

The course is great with plenty of knowledge. A little defect is about description about assignment. As the forum discussed, several quizzes may confusing.

by Shane C

2020年5月18日

concepts in the videos are well presented. additional readings from the textbook are helpful to cement concepts not explained as thoroughly in the videos

by Hilmi E

2020年2月16日

I really enjoyed attending this course. It is foundational material for anyone who wants to use graphical models for inference and decision making..

by Nimo F B

2020年9月10日

Great content and easy to pick up. Only issue was with downloaded Octave software. Does not work, despite multiple downloads on different machines

by Roman S

2018年3月20日

A good introduction to PGM, from very basic concepts to some move in-depth features. A big disadvantage is Matlab/Octave programming assignments.

by Serge S

2016年10月18日

Thanks to this course, Probabilistic Graphical Models are not anymore an esoteric subject! I am really looking for the second part of the course.

by Jack A

2017年11月5日

The class was very exciting and challenging, but I felt the programming assignments weren't dependent on understanding the classwork at all.

by François L

2020年3月16日

Really interesting contents but it would be great to have the exercises in a more up to date programming environment (python for instance)

by Gorazd H R

2018年7月7日

A very demanding course with some glitches in lectures and materials. The topic itself is very interesting, educational and useful.

by Ashwin P

2017年1月9日

Great material. Course mentors are nowhere to be found and some of the problems are hard, so I'd have liked to see some guidance.

by Forest R

2018年2月20日

Excellent introduction into probabilistic graph models. Introduced me to Baysian analysis and is quite helpful for my work.

by Иван М

2020年4月26日

Great course, would be nicer if exercises were in Python or R and if software from first honours task worked on Mac.

by Victor Z

2018年12月22日

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

by Luiz C

2018年6月26日

Good course, quite complex, wish some better quality slides, and more quizzes to help understand the theory

by Saurabh N

2020年3月24日

The coding assignments can be compulsory too.

Maybe not as vast, but maybe interleaved with the quizzes

by Werner N

2016年12月28日

Very good course. It should contain more practical examples to make the material better to understand.

by Haitham S

2016年11月24日

Great course, however, the honors track assignments are a bit too tedious and take lots of time.

by Kevin W

2017年1月17日

The course is pretty good. I love the way that the professor led us into the graphical models.

by Péter D

2017年10月29日

great job, although the last PA is a huge pain / difficulty spike - more hints would be nice

by Andres P N

2018年6月27日

There are many error in the implementations for octave. Aside from that, the course is fine

by Ahmad E

2017年8月20日

Covers some material a little too quickly, but overall a good and entertaining course.

by Soteris S

2017年11月27日

A bit more challenging than I thought but very useful, and very well structured

by mathieu.zaradzki@gmail.com

2016年10月4日

Great and well paced content.

Quizzes really helps nailing the tricky points.

by Caio A M M

2016年12月2日

Instructor is engaging in her delivery. Topic is interesting but difficult.