Probabilistic Graphical Models 1

Previous knowledge on basic probability theory and machine learning is highly recommended.

Definitely more challenging than the Machine Learning course material.

Definitely not for those without prior experience in machine learning, or statistics in general.

Before I took this course I took the Stanford Machine Learning course, which I greatly enjoyed.

A great course, a must for those in the machine learning domain.

Excellent course - it's incredible how many Machine Learning models are expressed under the umbrella of PGMs.

This course is very help for who have to study anything the respect of machine learning example, which is a thing much used in every day and in the new context of new industries 4.0, and the studies of probabilistcs graphical can help who need to develop new programs each times more efectiviness and best.

lectures not good(i mean not detailed) The material is really important and helpful for many concepts of Machine Learning.

Octave is an okay choice, and I suspect might have to do with Andrew Ng original choice to use it for his own machine learning course.

Great course Broad introduction to general issues Dear Madam thanks a lot for the course.This course - in addition to Machine Learning, by Andrew Ng Sir, are perhaps most comprehensive courses.This course covers a lot over a period of 5 weeks.

I love the way that the professor led us into the graphical models.

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.

The honors assignments are interesting, which instruct you to implement graphical models from scratch to solve problems in real world using Matlab or Octave.

If you are interested in graphical models, you should take this course.

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 found the introduction to probabilistic graphical models (PGMs) and their properties struck a nice balance between intuition and formalism.

Thanks to this course, Probabilistic Graphical Models are not anymore an esoteric subject!

It is foundational material for anyone who wants to use graphical models for inference and decision making.. Well presented course!

Good way to learn Probabilistic Graphical Models in practical Well-structured content, engaging programming assignments in honors track.

There are numerous materials you can find online to learn about Graphical Models than spending time & money on this.

The video lectures are really good and are useful for guiding you through Probabilistic Graphical Models book.

Thanks Daphne Koller, this is really motivating :) This is an amazing course, and taught by an extremely talented and accomplished professor.

Very well instructed by Prof Daphne Koller.

Comprehensive programming assignment with honour content and quizzes help to make yourself very familar with the topics: #bayesiannetwork #gibbssampling #intercasualreasoning #markovproccess #markovchain #OCR Daphne Koller @DaphneKoller , as Coursera co-funder, made her best to show the capabilities of the platform.

Prof Daphne Koller is one of the very few authority on this subject.

Daphne Koller is an outstanding lecturer and I was very impressed with the quality of provided material.

Daphne Koller is very good at explaining complicated ideas in an intuitive way.

To sum up, prospective students should take into account that the course is quite advanced and several background in probability, statistics, machine learning and algorithms required if you going to sign up for the PGM class =) Lectures and videos available for free but graded assignments and verified certifcate is paid option.

I found well structured contend of these rare probabilistic methods (Actually this is the only reasonable course in this approach online) I think this course is quite useful for my own research, thanks Cousera for providing such a great course.

Finally, a highly respect to Prof. Koller who provide the course in such a theoretical depth.

Prof. Koller does a great job explaining the concepts and uses up-to-date and useful examples.

Prof. Koller is knowledgeable and presented the materially logically.

Prof. Koller is an excellent lecturer, yet moves fast, and you'll need to do reading to fill in the gaps.

I also purchased the book written by Prof. Koller and Prof Friedman and I am going to continue my study on this subject.

Prof. Koller is exceptional.

Highly recommended.

In general, it is highly recommended.

Really well structured course.

I really enjoyed the content of this course.

Aside from that, the course is fine Good course, quite complex, wish some better quality slides, and more quizzes to help understand the theory Even though this is the most difficult course I have ever taken in Coursera, I really enjoyed the process.

Both test cases and feedback on failed submissions were woefully inadequate.

Some of the quizzes were also frustrating, featuring what I consider to be "gotcha" questions geared more to creating a grading curve than to measuring understanding of the material.Advice to course staff: (1) Please provide more test cases on coding assignments (2) Please provide better feedback in submission reports (3) Please monitor the discussion boards more actively for unanswered questions (4) If you want to provide an externally linked executable you intend students to run from Matlab, it's not reasonable to give a 32 bit file in 2017 and send us down a rabbit hole where you suggest we build the executable from source, which in turn requires us to build the boost library from source.