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286 Bewertungen

Über den Kurs

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....

Top-Bewertungen

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).

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126 - 150 von 280 Bewertungen für Probabilistic Graphical Models 1: Representation

von Ziheng

Nov 14, 2016

Very informative course, and incredibly useful in research

von Ingyo C

Oct 04, 2018

What a wonderful course that I haven't ever taken before.

von Renjith K A

Sep 23, 2018

Was really helpful in understanding graphic models

von Roger T

Mar 05, 2017

very challenging class but very rewarding as well!

von 吕野

Dec 26, 2016

Good course lectures and programming assignments

von Mahmoud S

Feb 25, 2019

Very good explanation and excellent assignments

von Lilli B

Feb 02, 2018

Brilliant content and charismatic lecturer!!!

von Fabio S

Sep 25, 2017

Excellent, well structured, clear and concise

von llv23

Jul 19, 2017

Very good and excellent course and assignment

von Parag H S

Aug 14, 2019

Learn the basic things in probability theory

von Jonathan H

Nov 25, 2017

This course is hard and very interesting!

von Shengliang X

May 29, 2017

excellent explanations! Thanks professor!

von Alexander K

May 16, 2017

Thank you for all. This is gift for us.

von Chahat C

May 04, 2019

lectures not good(i mean not detailed)

von Harshdeep S

Jul 19, 2019

Excellent blend of maths & intuition.

von NARENDRAN

Mar 07, 2020

Very good explanation on the subject

von Jui-wen L

Jun 21, 2019

Easy to follow and very informative.

von Miriam F

Aug 27, 2017

Very nice and well prepared course!

von Gary H

Mar 28, 2018

Great instructor and information.

von Subham S

Apr 29, 2020

I enjoyed the course very much!

von George S

Jun 18, 2017

Excellent material presentation

von 郭玮

Apr 26, 2019

Really nice course, thank you!

von hyesung J

Oct 10, 2019

So difficult. But interesting

von Jinsun P

Jan 17, 2017

Really Helpful for Studying!

von Shengding H

Mar 10, 2019

A very nice-designed course