Probabilistic Graphical Models 1: Representation(으)로 돌아가기

# 스탠퍼드 대학교의 Probabilistic Graphical Models 1: Representation 학습자 리뷰 및 피드백

4.7
1,180개의 평가
255개의 리뷰

## 강좌 소개

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....

## 최상위 리뷰

##### 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).

필터링 기준:

## Probabilistic Graphical Models 1: Representation의 248개 리뷰 중 226~248

교육 기관: Paul C

Oct 31, 2016

I found plenty of useful information in this course overall but lectures often spent too much time dwelling on the detail of simpler concepts while more complex areas, and sometimes critical information that was later built upon, were only touched briefly or sometimes skipped entirely. I missed a sense of continuity as we skipped from model to model with a minimum of time spent on how the models complement each other and their relative strengths and weaknesses in application.

The way data structures were defined in the code was particularly difficult to deal with. The coding exercises all suffered as a result. It ended up taking way too much time to figure how to decode the data and trace logic around it. This meant that grasping concepts and learning from the questions came in a distant second priority to debugging.

Dr Koller mentioned that the material is aimed at postgraduates. I felt that the level of content covered here would just as easily be grasped by most undergraduates in technical disciplines if it had been delivered in a more structured manner with clearer progression across models (conceptually and mathematically) and better code examples. When delivering in this format, allowances need to be made for the facts that tutorial sessions do not exist and the possibilities for informal Q&A are limited so any gaps become very difficult for students to fill in themselves.

Despite the above shortcomings I'm glad I did the course and I would still recommend it to someone interested in graphical models as it does cover the basics well enough to make a decent start. I'm not sure whether or not I'd recommend the programming exercises as they are a significant time sink but at the same time, without spending time attacking the programming problems the concepts are not likely to gel based on the video and quizzes alone.

교육 기관: Nicholas E

Oct 29, 2016

The course was very interesting and thought-provoking. I found the introduction to probabilistic graphical models (PGMs) and their properties struck a nice balance between intuition and formalism. The discussions highlighted exciting aspects of their power in simplifying complex problems involving uncertainty. However, I still do not feel I could propose convincing PGMs for real-world problems. There are examples in the course, but they are far removed from being concrete applications. I would have preferred there be an in depth analysis of an application of PGMs in the literature over the lengthy programming assignments. I am an experienced programmer with over 5 years of experience in many languages including MATLAB/Octave and I sometimes found it uninspiring to solve toy problems, not due to the difficulty in using the programming language, but rather because after the assignment had been completed I felt I had not really learnt much more than I would have from just watching the lectures, although, if you are interested in getting experience with MATLAB/Octave, the programming assignments are good practice. I qualify this in stating that I have not yet completed the next two courses on PGMs; this course may present an essential foundation that is necessary for the upcoming courses, and in any case provoked my interest in learning more about them

교육 기관: Mahendra K

Oct 04, 2017

The course is highly theoretical. Would have been great if it was paced well and driven from real world examples. I am not saying that there are no examples. But it'd have been better if the concepts were driven via some real world examples instead of first talking about the concept and then its applications.

What would have been even better if Python was an option for PAs. Octave can't be used in industry setting where the amount of data is really large. Both Python and Octave should have been an option so that the student can decide for themself.

교육 기관: John E M

Apr 01, 2018

Lectures were OK and quizzes and exams appropriately difficult. But Labs were pretty difficult especially lab 4 which I ended up surrendering on. This means I didn't do the accompanying quiz and gave up on the possibility of honors recognition as well.

While labs don't have to be as hand-holding as the DeepLearning class by Coursera, it would be nice to get more help and maybe not submit errors for the parts I haven't tackled yet when submitting (as DeepLearning and MachineLearning courses figured out how to do).

교육 기관: Kervin P

Jan 05, 2017

This is an amazing course, and taught by an extremely talented and accomplished professor. I believe it's a must for anyone in AI/ML or Statistical Inference. The problem is that you're essentially on your own the entire course. There isn't any community or TA help to speak off. And the project is done in Matlab, so you end up wrestling with Matlab or Octave instead of actually doing and learning. I still recommend the course, but that's only because the material is so extremely important.

교육 기관: Daniel S

Dec 11, 2019

Prof. Koller is exceptional. However, the focus of the course is toward the "theory" and less towards applications, unless one chooses to complete the Honors section of the course. I personally did not have the time to learn a new language syntax to attempt the Honors section...which is a shame. I do hope that this course is updated where R/Python replaces Octave/MatLab, because it would allow professional analysts more opportunity to explore the Honors content. Thanks!

교육 기관: Christos G

Mar 09, 2018

Quite difficult, not much help in discussion forums, some assignmnents had insufficient supporting material and explanations, challenging overall, I thought at least 3-4 times to abandon it.

교육 기관: Siavash R

Aug 11, 2017

For me this was a difficult course not because of the material, but because of the teaching style. I don't think Dr. Koller is a very good teacher.

교육 기관: Roman G

Nov 04, 2016

The audio is VERY VERY poor.

That makes it very hard to understand what Prof Kohler is trying to impart on us..

I often lost track

교육 기관: Xingjian Z

Nov 02, 2017

Fun topic. But the explanation of the mentor is somewhat vague and the material is sometimes outdated and misleading.

교육 기관: Ujjval P

Dec 13, 2016

Concepts covered in quiz and assignments are not covered well in the lecture videos, can be much better.

교육 기관: Jonathan K

Jan 26, 2018

Interesting and useful material, but I found the lecturer unengaging.

교육 기관: Michel S

Jul 14, 2018

Good course, but the material really needs a refresh!

교육 기관: Robert M

Feb 06, 2018

Started off well. Finished poorly

교육 기관: Peter

Sep 29, 2016

The content seems to be excellent regarding "what" is presented. But sadly the sound quality is rather bad: Sounds like an age-old valve radio with A LOT of dropouts. And Professor Daphne is an agile and therefore less disciplined speaker which lessens the understandability of her speech in conjunction with the poor sound quality furthermore. Especially for me as a non-native foreign english speaker it is very hard to follow. And now I am at one point in the course, that is "Flow of Probalistic Influence", where she explains a concept without explaining what is meant with the used underlying notions "flow" and "influence" which makes me difficult to understand what is going on. That means in my point of view that the slides are not sufficiently prepared. Although I'm very interested in the topic I am asking myself after the first view videos if I should continue or drop because my cognitive capacitity is for me to worthful to use it for the decoding of badly prepared and presented material. Ok, my decision heuristic in such cases is "Use the hammer not the tweezers!". Therefore I have dropped. Please improve the state of this class from beta to release. Then I will come back.

교육 기관: Jennifer H

Dec 16, 2019

Quite abstract. A solid mathematical grounding, but largely devoid of practicalities. Optional exercises are quite basic, and don't get to the heart of the matter. Lectures are confusing, as undefined terminology come up out of the blue, and key concepts aren't clearly explained.

교육 기관: Michael G

Feb 05, 2017

The support by the mentors could be much better. Because of the missing support I was not able to solve the assignments under Windows with Octave. I had to buy Matlab. (-2)

It seems to me that the course is very difficult to complete without additional sources. (-1)

교육 기관: Benjamin B

Apr 12, 2018

Did not like how the concepts were introduced, it felt like learning theory for the sake of theory.

교육 기관: Ashok S

Mar 30, 2018

It is hard to follow the course without a book, and the book is expensive.

교육 기관: Alexey G

Nov 06, 2016

It is impossible to submit quizes and programming assignments without purchasing the course. In my view this defies the goal of Coursera to provide accessible education anywhere in the world!

교육 기관: Casey C

Nov 01, 2016

Superficial coverage of quiz and final exam material in the video lectures. Without getting the textbook and reading it in depth, it is difficult to do well in this class.

교육 기관: Jabberwoo

Jun 24, 2017

Lectures are awful.

교육 기관: Belal M

Sep 08, 2017

A very dry course.