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Opiniones y comentarios de aprendices correspondientes a Probabilistic Graphical Models 2: Inference por parte de Universidad de Stanford

4.6
370 calificaciones
56 revisiones

Acerca del Curso

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....

Principales revisiones

AT

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.

AL

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.

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26 - 50 de 56 revisiones para Probabilistic Graphical Models 2: Inference

por Chan-Se-Yeun

Jan 31, 2018

I kind of like the teacher. She can always explain complicated things in a simple way, though the notes she writes in the slides are all in free style. Loopy belief propagation and dual decomposition are the best things I've learnt in this course. I've met them before in some papers, but I found it extremely hard to understand then. Now I gain some significant intuition of them and I'm ready to do further exploration. Anyway, I'll keep on learning course 3 to achieve my first little goal in courser.

por Sriram P

Jun 24, 2017

Had a wonderful and enriching fun filled experience, Thank you Daphne Ma'am

por Alireza N

Jan 12, 2017

Excellent!

por Rishi C

Oct 28, 2017

Perhaps the best introduction to AI/ML - especially for those who think "the future ain't what it used to be"; the mathematical techniques covered by the course form a toolkit which can be easily thought of as "core", i.e. a locus of strength which enables a wide universe of thinking about complex problems (many of which were correctly not thought to be tractable in practice until very recently!)...

por Simon T

Sep 14, 2017

Great job Prof. Koller!

por Musalula S

Aug 02, 2018

This is a great course

por Anthony L

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.

por Ayush T

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.

por 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.

por 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).

por 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.

por 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.

por Rajeev B

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.

por 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.

por 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%

por 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.

por G.K.Vikram

Jul 24, 2017

very good course

por 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

por Kalyan D

Nov 05, 2018

Great introduction.

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

por 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.

por Siwei Y

Jan 17, 2017

有幸能听到COURSERA创始人的课,确实领略了一下大牛人的风采。但是从教课这个层面来看, 我相信有人能教得更好。 最可惜的是编程作业,我根本不能submit 。上课的内容和作业脱节很明显。 而且很多时候, 基本没有编程方面的支持(可以从论坛的人气就可以看出了), 学生几乎无从下手总的来说,此课过多的侧重于抽象层面的东西。

por Tomer N

Jun 20, 2018

The Programming assignment must be updated and become relevant... They are way too hard and not friendly...

por Thomas W

May 05, 2017

Great but it would be nice to have some introduction to approximate inference methods as well.

por Chris V

Dec 14, 2016

Content is good but honours assignments are unclear and no help from mentors in the discussion forums - more time-consuming than they should be

por fan

Nov 20, 2016

Can't get score for free!!!