Chevron Left
Retour à Probabilistic Graphical Models 2: Inference

Avis et commentaires pour d'étudiants pour Probabilistic Graphical Models 2: Inference par Université de Stanford

433 évaluations
63 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 second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem....

Meilleurs avis


Aug 23, 2019

Just like the first course of the specialization, this course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.


Aug 20, 2019

I have clearly learnt a lot during this course. Even though some things should be updated and maybe completed, I would definitely recommend it to anyone whose interest lies in PGMs.

Filtrer par :

26 - 50 sur 63 Avis pour Probabilistic Graphical Models 2: Inference

par Arthur C

Jul 19, 2017

Difficult, but it makes you think a lot!

par chen h

Feb 06, 2018

Interest but difficult.

par Simon T

Sep 14, 2017

Great job Prof. Koller!

par Musalula S

Aug 02, 2018

This is a great course

par Wei C

Mar 06, 2018

good way to learn PGM,

par Alexander K

Jun 03, 2017

Thank You for all.

par 王文君

May 21, 2017

Awesome class!

par 郭玮

Nov 13, 2019

Very helpful.

par Anderson d R L

Nov 03, 2017

Great course!

par Alireza N

Jan 12, 2017


par hanbt

Jun 08, 2018

Very good

par Péter D

Nov 14, 2017


par Michael K

Dec 24, 2016

The course lectures are even better than PGM I, as it appears that Professor Koller has recorded some material recently that helps fill in small holes from the previously recorded lectures. Hopefully she'll have time to clean up PGM I in the near future for future students.

This course is another tour-de-force for debugging, though it definitely made me a better programmer (I'm intermediate). I wish that the Discussion Boards were more active, and it's a shame that the Mentors were Missing In Action. On the one hand, the programming instructions were sometimes a bit vague, which made the assignments less like assignments are more like research projects. For these 2 reasons, the course is 4-star rather than 5-star.

Still, it's a lot better than trying to learn this out of the book by oneself. Some say enrollment has dropped off since they began charging for getting access to Quizzes and Programming Assignments. Or it may be attrition, as these are pretty challenging (and well taught) courses. I'm very happy to support this course financially, as it's loads cheaper than what I'd be paying if I were back at Stanford.

Like PGM I, I strongly recommend doing the Honors Programming Assignments, as it's really the way to learn the material well.

par Amine M

May 14, 2019

The course content is great. The lecturer is great as she explains intuitively! Unfortunately, the programming assignments are horrible. Code is being provided without any mentioning in the PDF problem sheet. Moreover, most of the functions provided are not commented at all. Testing and debugging your method is made incredibly difficult because of the cryptic infrastructure of the test samples and too many typos in almost every problem sheet, which does not even get corrected even though many course takers pointed out these typos years ago. Finally, the forum for discussions is basically dead. If you do not get something there is no hope for you but to give up because mentors are not available in the forum. All in all, this class is really great but does not deliver enough content and information in order to be able to solve the programming assignment problems.

par Diogo P

Oct 24, 2017

Unfortunately, in my opinion, this course is not as well structured as the first course (PGM1: structure). There are some bugs/issues with the PAs code that should have been fixed and the course material could focus a bit more on the case of continuous random variables (which are almost ignored throughout the course). It is still a great and totally worth it course, though. Highly recommended for machine learning post-graduate students.

par Akshaya T

Mar 14, 2019

The material is quite good and a good depth for a first pass. I would definitely have liked that there be some structure slides at the start of the lecture set. Saying -- this is what we will learn in week 1 week 2.. so on, so I know what I am getting into. The way it is designed now, I am swimming in the water so deep that I can barely see 1 week away.

par Diego T

Jun 09, 2017

Great Course, not five stars just because probabbly it was too much content for the period of time we had the Course. I've got no complaints about the amount of content, but some of concepts were missing and the Programming Assignments were not so well described, sometimes I couldn't understand what to do.

par Michael G

Dec 14, 2016

The course reminds me of my math lessons: lots of formulas and apparatus but little motivation (except in the optional videos). As in the first part of the specialization the advised book about PGM is highly recommended. To pass the final exam the book or at least some research papers are necessary (-1).

par Siwei G

Jun 15, 2017

it is a great class. but the presentation of the materials could be better: maybe each unit should start with a review of the key concepts we learned before? maybe a slide on motivation of the work before we dive deep into the math? but again, this is a great class! recommended 100%

par Rajeev B A

Dec 23, 2017

Unlike other Coursera courses, this specialization covers a lot of conepts accompanied with programming assignments. Since the programming assignments are pre-filled, its a bit tough to understand the style. It would be great if some form of explanation if offered.

par Maxim V

May 06, 2020

A great course, and programing assignments add *a lot* of value to it. As with the other courses of this specialization, there is virtually no assistant support in discussion forums and very little discussion in general.

par Luiz C

Aug 01, 2018

Very good course. Subject is quiet complex: lack of concrete examples to make sure concepts well understood. Had to review each the Course twice to understand concepts well

par Rishabh G

May 16, 2020

Great course. The assignments are old and are not worth doing it. But the content is good for those who are interested in Probabilistic Graphical Models basics.

par Gorazd H R

Jul 07, 2018

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

par Kalyan D

Nov 05, 2018

Great introduction.

It would be great to have more examples included in the lectures and slides.