返回到 Probabilistic Graphical Models 1: Representation

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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....

ST

2017年7月12日

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

2017年10月22日

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).

篩選依據：

創建者 Ahmad E

•2017年8月20日

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

創建者 Soteris S

•2017年11月27日

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

創建者 mathieu.zaradzki@gmail.com

•2016年10月4日

Great and well paced content.

Quizzes really helps nailing the tricky points.

創建者 Caio A M M

•2016年12月2日

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

創建者 Michael B

•2019年12月12日

Honors seems like a must to full instill concepts/implementation

創建者 Anshuman S

•2019年5月7日

I would recommend adding some supplemental reading material.

創建者 Jhonatan d S O

•2017年5月25日

Rich content and useful tools for applying in real problems

創建者 Vahan A

•2020年5月31日

Please, provide programming assignments on Python or C++

創建者 Alberto C

•2017年12月1日

Theory: Very interesting. Assignments: not so useful.

創建者 Yuanduo H

•2020年1月19日

Five stars minus the week 4 coding homework

創建者 Arthur

•2017年1月8日

More feedback from TA would be appreciated

創建者 Ian M C

•2018年12月26日

Writing on the ppt is not clear to see.

創建者 Soumyadipta D

•2019年7月16日

lectures are too fast otherwise great

創建者 sunsik k

•2018年7月31日

Broad introduction to general issues

創建者 Tianyi X

•2018年2月20日

Lack of top-down review of the PGM.

創建者 Sunil

•2017年9月12日

Great intro to probabilistic models

創建者 Nikesh B

•2016年11月6日

Excellent

創建者 Tianqi Y

•2019年6月19日

too hard

創建者 Yashwanth M

•2020年1月5日

Good

創建者 Ricardo A M C

•2021年1月9日

ok

創建者 Paul C

•2016年10月31日

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.

創建者 Nicholas E

•2016年10月29日

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

創建者 Mahendra K

•2017年10月4日

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.

創建者 John E M

•2018年3月31日

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).

創建者 Kervin P

•2017年1月5日

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.