Chevron Left
Retour à Probabilistic Graphical Models 1: Representation

Avis et commentaires pour d'étudiants pour Probabilistic Graphical Models 1: Representation par Université de Stanford

1,193 évaluations
259 avis

À propos du cours

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....

Meilleurs avis


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!!


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).

Filtrer par :

101 - 125 sur 252 Avis pour Probabilistic Graphical Models 1: Representation


Jan 06, 2017

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

par Arjun V

Dec 04, 2016

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

par CIST N

Oct 30, 2019

Good way to learn Probabilistic Graphical Models in practical

par Prazzy S

Jan 20, 2018

Challenging! Regret not doing the coding assignment for honors

par Gautam B

Jul 04, 2017

Great course loved the ongoing feedback when doing the quizes.

par Achen

May 06, 2018

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

par albert b

Nov 04, 2017

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

par Arthur C

Jun 04, 2017

Super useful if you want to understand any probability model.

par Sriram P

Jun 24, 2017

Had a wonderful learning experience, Thank You Daphne Ma'am.

par Pablo G M D

Jul 18, 2018

Outstanding teaching and the assignments are quite useful!

par Ziheng

Nov 14, 2016

Very informative course, and incredibly useful in research

par Ingyo C

Oct 04, 2018

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

par Renjith K A

Sep 23, 2018

Was really helpful in understanding graphic models

par Roger T

Mar 05, 2017

very challenging class but very rewarding as well!

par 吕野

Dec 26, 2016

Good course lectures and programming assignments

par Mahmoud S

Feb 25, 2019

Very good explanation and excellent assignments

par Lilli B

Feb 02, 2018

Brilliant content and charismatic lecturer!!!

par Fabio S

Sep 25, 2017

Excellent, well structured, clear and concise

par llv23

Jul 19, 2017

Very good and excellent course and assignment

par Parag H S

Aug 14, 2019

Learn the basic things in probability theory

par Jonathan H

Nov 25, 2017

This course is hard and very interesting!

par Shengliang

May 29, 2017

excellent explanations! Thanks professor!

par Alexander K

May 16, 2017

Thank you for all. This is gift for us.

par Chahat C

May 04, 2019

lectures not good(i mean not detailed)

par Harshdeep S

Jul 19, 2019

Excellent blend of maths & intuition.