Voltar para Probabilistic Graphical Models 1: Representation

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1,380 classificaĂ§Ă”es

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

12 de jul de 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

22 de out de 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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por oilover

âą2 de dez de 2016

èćžćŸæŁïŒïŒ

por ćä»çȘ

âą31 de out de 2016

äžéçäžéšèŻŸ

por Accenture X

âą12 de out de 2016

Great

por Ludovic P

âą29 de out de 2017

I wish I could give 4 and a half star to this course.

On the positive side : there is a lot of value in this course. Professor Koller succeeds in introducing us to PGM representations in a few weeks. IMHO, one should really do all the exercices "for a mention". Without them, this course lacks "hands on" sessions, and is much less interesting. Most programming exercises are great, and the companion quiz are really a plus.

When I followed Professor Ng programming exercises, I was both delighted and frustrated. Delighted because I learned a lot of things. Frustrated because it was sometimes really too easy.

This is not the case for most exercices there. I find them so well prepared, so crafted that I often learned a lot of my first wrong submissions of quiz of programming exercices.

On the negative side : the quality of the sound recordings is sometimes not really good. That is especially true in the first videos. That should not stop you from following this great course ! Some programming exercices were a bit frustrating because their difficulty is more in knowing octave tips and tricks than in PGM. In addition, and this is more embarassing, some exercices do not work, like in Markov Network for OCR https://www.coursera.org/learn/probabilistic-graphical-models/programming/dZmtj/markov-networks-for-ocr I had, as other students, to disable some features and to blindly submit my ansmwers.

Also, some exercises were difficult for me because of very precise English. I guess it might be difficult for native speakers to handle that, but as this course seems to have an international audience, it would be great.

I feel that raising this great course from 4 stars to 5 stars would not require much efforts. Prepare better recordings of the few videos that have really bad sound. Correct those small bugs in exercises. Simplify some English wordings.

I, however, advise this course to all persons interested in this field. And I intend to follow the next course, on inference.

por Jonathan H

âą25 de jun de 2017

Excellent course. The video lectures are challenging (had to keep my finger on the pause key) even if you're familiar with the math, since the instructor encapsulates concepts in an amazingly concise manner. This pays off with a lot of "Aha!" moments as strong concepts are combined to create insights, especially starting around week 3. I'm already in love with this subject after 1 part

It would have been nice to have more worked homework problems, since this is a math course. But, this is not necessary to pass the class or understand the concepts. I've purchased Prof Koller's text on PGM and hope to solidify some of the intuitions I'm missing shortly.

Taking off a star because the test cases and grading software for the honors homework assessments were clearly low effort and sometimes incorrect. There were a lot of cases where functions passed all the provided and automatic test cases despite major flaws (e.g. not working for any cases besides n=1), which made it difficult to tell if things worked since the programming style is unique. The homework itself was super interesting and valuable, but I probably spent over 50% of the time fighting the grader instead of learning stuff. Given that I'm a professional programmer and completed most of the homework in 25-50% of the estimated time, I'm guessing that the average student wasted even more time with issues that are ultimately unrelated to our understanding of PGM.

por Hunter J

âą12 de jan de 2017

Before I took this course I took the Stanford Machine Learning course, which I greatly enjoyed. That course allows for the learning of difficult concepts in a way that I found less painful than working through a textbook. In this course there is a lot less video content, and the coding assignments are less interesting. Expect to spend a lot of time understanding the nuances of the code that the instructional team has developed, and be prepared to really pore over the gritty aspects of Octave or MATLAB. If you're serious about this course I suggest buying the accompanying book. The slides are not easy to understand without the audio narration, which makes them difficult to review, and unlike the case in the ML course, there are not a lot of readily available open introductions written on the topics.

por Stephen A

âą18 de mai de 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.

por Michael K

âą14 de nov de 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.

por george v

âą7 de jul de 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!

por AntĂŽnio H R

âą20 de jun de 2018

The video lectures are really good and are useful for guiding you through Probabilistic Graphical Models book. I did not like the honor track programming exercises, however. The problems seems artificial to me and they make use of very strange data structures (probably due to the adoption of matlab as programming language). You end up wasting a lot of time with unimportant points instead of exploring ideas and getting cool results. Furthermore, I don't think the programming exercises help to familiarize the user to any of standard tools for bayesian analysis (i.e. probabilistic programming languages and so on).

por Dat N

âą28 de mar de 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.

por Memo U

âą1 de jul de 2017

Thanks for offering this course, I have learned a lot. However, the course is quite confusing. Not everything is well defined so it is hard to answer some questions. The honors programming assignments are usually confusing in this manner. If you put in the effort to understand it thou, it can be done. To be honest thou, some misunderstanding could be given to my lack of understanding the material at first. At the same time my lack of understanding is probably caused by the course material being not so well defined. Maybe it would help if one spends more time reading the text book.

por Brian E

âą27 de ago de 2020

The content is good. I'm excited to learn enough about these techniques to use them in my projects at work. The quizzes seem overly complicated and have trickily worded questions, especially for the honors parts. The programming assignments are tough, which is OK, but the bugs in the submission process make completing them very frustrating. The forums are full of people trying to reach admins / mentors to get things fixed without success.

por Shantanu B

âą3 de set de 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.

por æäżćź

âą8 de nov de 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!

por Laurent G

âą5 de mai de 2020

This is overall a great course. It required me a bit of reading outside of the course material, and fail on quizzes a few times before understanding, but it is was very much worth the effort. However, the assignments in MATLAB and IAMSAM feel dated. As much as I would like to exercise the newly acquired knowledge with exercises MATLAB is particularly irritating after having used other languages.

por Andreas B

âą21 de jan de 2021

Lectures very good, but the code in the programming assignments is awful.

Having done the first few programming assignments, I decided to switch to recode and do the programming excercises in python/numpy/scipy etc.

The code definitely should get an update, especially because for instance tensorflow starts to integrate tensorflow probability.

por Sina T

âą26 de set de 2021

Vâideo lectures were clear and the course content was detailed and explained clearly. I take one star off because some of the material needed for the quizzes wasn't in the main course material; for example, the sum-product algorithm was mentoned in one of the quiz questions, but wasn't mentioned in the main material.

por Frank D

âą16 de mai de 2021

The course Information tell us that we can buy Professor Koller's book with a discount from Amazon. I tried but amazon didn't like the code. Is this information still true.

I regularly wanted to go and read a book to understand what more of what I was being told but at ÂŁ70 a pop this is a challenging purchase

por Zhen L

âą15 de nov de 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....

por Abraham R

âą26 de out de 2020

It is a magnificent course, terrific information and lectures. Nonetheless, please update your programing exercises . Consider utilising either Matlab, R, Python or GenIE. SamIAM is terrible for the installation and ,as in my experience, it simply did not work.

Regards

Abraham

por LV

âą21 de mar de 2018

Some of the examples are a bit confusing. I mostly used logic to solve these versus following a formula. Octave was fine but I didn't know how to use SAMIAM and so gave up on the coding assignments since PGMs aren't a focus area for me except for general theoretical knowledge.

por Roland R

âą20 de dez de 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.

por Hanbo L

âą29 de abr de 2017

In general this is a good introductory course. You should read the book if you want more in-depth knowledge in this field. I feel that some of the concepts can be expanded a little more, like local structure in Markov model. Overall, this is a great course.

por Rick

âą20 de abr de 2017

Everything is explained very clearly throughout the course, and the structure they use to teach the subject , from basics to advanced material, is especially helpful. Would recommend this course to anyone with an interest in probabilistic modelling.

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