Probabilistic Graphical Models 1: Representation(으)로 돌아가기

4.7

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241개의 리뷰

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....

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).

필터링 기준:

교육 기관: Boxiao M

•Jun 28, 2017

The lecture was a bit too compact and unsystematic. However, if you also do a lot of reading of the textbook, you can learn a lot. Besides, the Quiz and Programming task are of high qualities.

교육 기관: Ahmad E

•Aug 20, 2017

Covers some material a little too quickly, but overall a good and entertaining course.

교육 기관: Zhen L

•Nov 16, 2016

The course gives an good introduction of PGM. The highlights are the well-designed quizzes and assignments. But the videos of lectures are not good enough. It's too fast and some key concepts are not clearly explained.

After looked into another course on coursera, I add a star for this....

교육 기관: Alberto C

•Dec 01, 2017

Theory: Very interesting. Assignments: not so useful.

교육 기관: Caio A M M

•Dec 03, 2016

Instructor is engaging in her delivery. Topic is interesting but difficult.

교육 기관: 李俊宏

•Nov 09, 2017

This is a tough course so it was split into 3 parts. I've learned some ideas about bayesian network and markov model. The major problem about this course is the programming assignment, which is poorly maintained. Daphne Koller is very brilliant but this makes it hard for people to catch up with her, especially for people whose mother language is not English. After all, this is an interesting course!

교육 기관: Andres P N

•Jun 27, 2018

There are many error in the implementations for octave. Aside from that, the course is fine

교육 기관: Soteris S

•Nov 27, 2017

A bit more challenging than I thought but very useful, and very well structured

교육 기관: Forest R

•Feb 20, 2018

Excellent introduction into probabilistic graph models. Introduced me to Baysian analysis and is quite helpful for my work.

교육 기관: Tomasz L

•May 12, 2019

Great course! Lectures are clear and comprehensive. Quizzes really check knowledge and are challenging. In the programming assignments the main focus is put on implementation of PGM algorithms and not on technical aspects of Octave/Matlab. Some changes could be made in Programing Assignment 4 to make description and provided code easier to understand.

교육 기관: Anshuman S

•May 08, 2019

I would recommend adding some supplemental reading material.

교육 기관: tyang16

•Jun 20, 2019

too hard

교육 기관: Soumyadipta D

•Jul 16, 2019

lectures are too fast otherwise great

교육 기관: Michel S

•Jul 14, 2018

Good course, but the material really needs a refresh!

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: 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: 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: 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: 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: Robert M

•Feb 06, 2018

Started off well. Finished poorly

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: 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: Jonathan K

•Jan 26, 2018

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

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.