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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: 101 - 125 / 295 レビュー

by Una S

2020年7月24日

Amazing!!! Loved how Daphne explained really complex materials and made them really easy!

by liang c

2016年11月15日

Great course. and it is really a good chance to study it well under Koller's instruction.

by AlexanderV

2020年3月9日

Great course, except that the programming assignments are in Matlab rather than Python

by Ning L

2016年10月17日

This is a very good course for the foundation knowledge for AI related technologies.

by Hong F

2020年6月21日

Hope there are explanations of the hard questions (marked by *) in the final exam.

by Abhishek K

2016年11月6日

Difficult yet very good to understand even after knowing about ML for a long time.

by chen h

2018年1月20日

The exercise is a little difficult. Need to revise several times to fully digest.

by Isaac A

2017年3月23日

A great introduction to Bayesian and Markov networks. Challenging but rewarding.

by 庭緯 任

2017年1月10日

perfect lesson!! Although the course is hard, the professor teaches very well!!

by Alejandro D P

2018年6月29日

This and its sequels, the most interesting Coursera courses I've taken so far.

by Naveen M N S

2016年12月13日

Basic course, but has few nuances. Very well instructed by Prof Daphne Koller.

by Amritesh T

2016年11月25日

highly recommended if you wanna learn the basics of ML before getting into it.

by Pouya E

2019年10月13日

Well-structured content, engaging programming assignments in honors track.

by David C

2016年11月1日

If you are interested in graphical models, you should take this course.

by Camilo G

2020年2月4日

Professor Koller does an amazing job, I fully recommend this course

by PRABAL B D

2018年9月1日

Awesome Course. I got to learn a lot of useful concepts. Thank You.

by Pham T T

2019年12月13日

Excellent course! This course helps me so much studying about PGM!

by Lik M C

2019年1月12日

A great course! The provided training clarifies all key concepts

by Sivaramakrishnan V

2017年1月6日

Great course. Thanks Daphne Koller, this is really motivating :)

by Arjun V

2016年12月3日

A great course, a must for those in the machine learning domain.

by CIST N

2019年10月30日

Good way to learn Probabilistic Graphical Models in practical

by Prazzy S

2018年1月20日

Challenging! Regret not doing the coding assignment for honors

by Gautam B

2017年7月4日

Great course loved the ongoing feedback when doing the quizes.

by Achen

2018年5月6日

a bit too hard if you don't have enough probability knowledge

by albert b

2017年11月4日

Best course anywhere on this topic. Plus Daphne is the best !