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Probabilistic Graphical Models 1: Representation(으)로 돌아가기

스탠퍼드 대학교의 Probabilistic Graphical Models 1: Representation 학습자 리뷰 및 피드백

4.7
1,078개의 평가
241개의 리뷰

강좌 소개

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....

최상위 리뷰

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!!

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).

필터링 기준:

Probabilistic Graphical Models 1: Representation의 234개 리뷰 중 151~175

교육 기관: 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)

교육 기관: 杨涛

Mar 27, 2019

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

교육 기관: 郭玮

Apr 26, 2019

Really nice course, thank you!

교육 기관: Yue S

May 09, 2019

Great course!

교육 기관: HOLLY W

May 25, 2019

课程特别好,资料丰富

교육 기관: Jui-wen L

Jun 21, 2019

Easy to follow and very informative.

교육 기관: Nijesh

Jul 18, 2019

Thanks for offering

교육 기관: 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.

교육 기관: Harshdeep S

Jul 19, 2019

Excellent blend of maths & intuition.

교육 기관: 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.

교육 기관: Parag H S

Aug 14, 2019

Learn the basic things in probability theory

교육 기관: Xiaojie Z

Dec 22, 2018

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

교육 기관: Myoungsu C

Dec 26, 2018

Writing on the ppt is not clear to see.

교육 기관: Sunsik K

Jul 31, 2018

Broad introduction to general issues

교육 기관: Shantanu B

Sep 03, 2018

This course is a very essential learning step for people who want to learn and work with Baysean or Markov networks. I think that the course can be further improved by going a little slow on certain assertions or deductions which are fundamental to the subject. Those should be properly emphasized. But overall the assignments were challenging and actually made you think about the things taught in that corresponding video.

교육 기관: 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.

교육 기관: Sunil

Sep 12, 2017

Great intro to probabilistic models

교육 기관: Péter D

Oct 29, 2017

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

교육 기관: george v

Jul 07, 2017

very nice intuition from the professor Daphne Koller and "compact" in these lectures that dont exceed 15min each. really glad i did the first one, wish i did also the other two parts, certainly will when i find the time. Just as a comment, i mostly enjoyed the programming assignments. they are very well structured and in a very particular manner, which at the same time is the strong and the weak point of the assingment, since at times i undertsood something else than what the actual implementation was. anyway they were really a challenge, and whoever manages to do them should be glad with his work. Thank you prof. Koller for this course!

교육 기관: Akshaya T

Jan 16, 2018

Some tutorials need disambiguating documentation (upgrade :)) but otherwise, the course is really good. It would also help if there is a mention of what chapters to study from the book for every lesson -- in the slides.

교육 기관: 邓成标

Nov 30, 2017

The materials are very interesting, however, this professor speaks so fast that it is hard to grasp the deep theory. In overall, this course is great. And I really need to do the assignment to enhance my comprehension about the content.

교육 기관: Tianyi X

Feb 20, 2018

Lack of top-down review of the PGM.

교육 기관: Jhonatan d S O

May 25, 2017

Rich content and useful tools for applying in real problems

교육 기관: Kevin W

Jan 17, 2017

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

교육 기관: serge s

Oct 18, 2016

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