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Avis et commentaires pour l'étudiant pour Probabilistic Graphical Models 1: Representation par Université de Stanford

4.7
1,150 notes
250 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 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....

Meilleurs avis

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).

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201 - 225 sur 243 Examens pour Probabilistic Graphical Models 1: Representation

par Surender K

Nov 07, 2016

Wonderful course with great material. Wish there were more examples in the material. Nonetheless cannot complain to get this course for free with SEE material and programming assignments (need to complete yet in this session)

par serge s

Oct 18, 2016

Thanks to this course, Probabilistic Graphical Models are not anymore an esoteric subject! I am really looking for the second part of the course.

par Stephen A

May 18, 2018

I really enjoyed this course. Prof Koller presents the material very well, and it's really interesting to see how probabilistic graphical model frameworks are underpinned mathematically. I thought it was a pretty tough course at points, and while the lectures are good I found having a copy of Prof Koller's textbook very useful.

I would give this course 5 stars, but I thought some of the programming assignments involved too much grappling with MATLAB rather than illuminating the principles in the lectures. Also, I think the order of the lectures may have been changed since the course was first run as there are occasional references to things that have not been covered at that point.

Overall though, very enjoyable. I'm looking forward to parts 2 and 3.

par Sunil

Sep 12, 2017

Great intro to probabilistic models

par Péter D

Oct 29, 2017

great job, although the last PA is a huge pain / difficulty spike - more hints would be nice

par 邓成标

Nov 30, 2017

The materials are very interesting, however, this professor speaks so fast that it is hard to grasp the deep theory. In overall, this course is great. And I really need to do the assignment to enhance my comprehension about the content.

par Tianyi X

Feb 20, 2018

Lack of top-down review of the PGM.

par Alain M

Nov 03, 2018

Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.

par Sunsik K

Jul 31, 2018

Broad introduction to general issues

par Shantanu B

Sep 03, 2018

This course is a very essential learning step for people who want to learn and work with Baysean or Markov networks. I think that the course can be further improved by going a little slow on certain assertions or deductions which are fundamental to the subject. Those should be properly emphasized. But overall the assignments were challenging and actually made you think about the things taught in that corresponding video.

par Soumyadipta D

Jul 16, 2019

lectures are too fast otherwise great

par Anshuman S

May 08, 2019

I would recommend adding some supplemental reading material.

par Michael B

Dec 12, 2019

Honors seems like a must to full instill concepts/implementation

par Roland R

Dec 20, 2017

Good course. Sometimes a little bit hard to follow. For example representation of probability functions as graphs (connection between factorisation of probabilty distribution and cliques in the graph). And I'm not sure If I can apply PGMs to real world problems now.

par Dat V N

Mar 28, 2018

The course helps me understand what a probabilistic graphical model is and how and why it works. One aspect I like the most about the course is the programming assignments. Those PA really make a lot of concepts clearer although sometimes you need to think carefully when the instruction is hard to follow. I think there should be more test case and expected results so that students know what is asked and to evaluate their own code. The instructor is generally clear but sometimes she goes too fast on certain concept. The course is hard but if you gives in time and effort you can complete it.

par Jhonatan d S O

May 25, 2017

Rich content and useful tools for applying in real problems

par Michael K

Nov 14, 2016

This excellent course is exceptional in that very few MOOCs are taught at this graduate level. Others have pointed out that while this is an introductory course to Probability Graphical Models, I would say that this is still an advanced course, with lots of prerequisites. Prof. Koller is an excellent lecturer, yet moves fast, and you'll need to do reading to fill in the gaps. I haven't been able to find a good book to accompany the course, as her book is pretty dry. I strongly recommend one complete all of the Honors assignments to get a lot out of the course. The discussion boards are not so active with plenty of unanswered questions. Doing the programming assignments will greatly enhance your skills in debugging.

par george v

Jul 07, 2017

very nice intuition from the professor Daphne Koller and "compact" in these lectures that dont exceed 15min each. really glad i did the first one, wish i did also the other two parts, certainly will when i find the time. Just as a comment, i mostly enjoyed the programming assignments. they are very well structured and in a very particular manner, which at the same time is the strong and the weak point of the assingment, since at times i undertsood something else than what the actual implementation was. anyway they were really a challenge, and whoever manages to do them should be glad with his work. Thank you prof. Koller for this course!

par Kevin W

Jan 17, 2017

The course is pretty good. I love the way that the professor led us into the graphical models.

par tyang16

Jun 20, 2019

too hard

par Nikesh B

Nov 06, 2016

Excellent

par Michel S

Jul 14, 2018

Good course, but the material really needs a refresh!

par Jonathan K

Jan 26, 2018

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

par 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.

par 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.