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

Jul 12, 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 22, 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).

筛选依据：

创建者 Douglas G

•Oct 24, 2016

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.

创建者 Venkateshwaralu S

•Oct 25, 2016

I loved every minute of this course. I believe I can now understand those gory details of representing an algorithm and comfortably take on challenges that require construction and representation of a functional domain. On a different note, nurtured a new found respect for the graph data structure!

创建者 Ryan D

•Jun 21, 2017

Quiz and Video Lecture content was good. Would have preferred different format for programming assignments. The 30 minute life time of programming assignment submission tokens was pretty inconvenient. Overall great course. Definitely more challenging than the Machine Learning course material.

创建者 Jorge P

•Feb 26, 2017

Brilliant course, extremely challenging. Prof. Koller does a great job explaining the concepts and uses up-to-date and useful examples. The quizzes are the hardest I've faced in Coursera, this course is no joke, it will take time, effort and taking notes to get through it.

创建者 Juan P J A

•May 11, 2022

Great content, explanations and flow . Explanations are dense sometimes so it requires to stop or replay the video. I gave up on the extra material given the problems with the automatic online submission (this should be updated). In general, I highly recommend it!

创建者 roi s

•Oct 29, 2017

I really like how Dafna is teaching the course, very clear!

It will be nice if their could be a following course that will show new frameworks and code that implements PGMs. Like the courses of deep learning where Andrew Ng is focusing mostly on the practical side.

创建者 Anurag P

•Jan 8, 2018

The course is quite hard, however it becomes easier if you follow the book along with course. Also, programming assignments need to improved, the bugs and known issues mentioned in forum should be incorporated to prevent people from wasting time on setup issues.

创建者 Yuxuan X

•Aug 7, 2017

Awsome course for Information/Knowledge Engineering. Although not necessary to finish all the honor assignments, it is highly recommended to implement them. Not only for comprehension, but also practice. You can actually apply them on your career or research.

创建者 Minh N

•Mar 1, 2017

Quite a steep learning curve. Definitely not for those without prior experience in machine learning, or statistics in general. Also, I would much appreciate it if more test cases were provided in the programming assignments to help with debugging.

创建者 Christophe K

•Oct 22, 2016

Very challenging course, but hey, if you are here, you are looking for that!

Lots of knowledge to absorb, but that leads you to a deep understanding on Probability Graphs properties.

I've learnt a lot and I really enjoyed taking this course.

创建者 Maxim V

•Apr 29, 2020

Basic but absolutely necessary knowledge (representation). Quizzes were surprisingly easy. The best (and in my opinion absolutely necessary) part are the honor assignments, they make the course not just a little but many times better.

创建者 José A R

•Sep 13, 2018

Excellent course. Very well explained with precise detail and practical material to consolidate knowledge.

This was my first approach to PGM and end it fascinated. Will look to learn more from this subject.

Thank you very much Daphne!!

创建者 Chatard J

•Nov 25, 2016

Une méthode pédagogique sans faille. Des contrôles et des exercices qui permettent d'approfondir ce qu'on apprend et de faire le point en permanence. Un merveilleux voyage dans le monde des Modèles Graphiques Probabilistes.

创建者 Justin C

•Oct 23, 2016

This was a fantastic introduction to PGM for a non-expert. It is well paced for an online course and the assignments provide enough depth to hone your knowledge and skills within the 5 week timeframe. Highly recommended.

创建者 KE Z

•Nov 23, 2017

All Programming Assignments are challenging (Bayesian net, Markov net/CRF, and decision making), but very essential to help understand how PGM works. I definitely will enroll the second course in this specialization.

创建者 Alexey K

•Nov 17, 2017

Thank you! It's simply incredible exercise for brain! :-) The best ever course here, which teaches one to really think and model, rather than merely click to choose most plausible answer ( like other courses do )

创建者 Supakorn S

•Apr 27, 2022

The instructor provide clear explanations and useful examples.

Reading the recomended resourses, including the books, are also help me to comprehend the course contents.

Great course overall, thanks

创建者 Ofelia P R P

•Dec 11, 2017

Curso muy completo que da conocimiento realmente avanzado sobre modelos gráficos probabilísticos. Aviso, la especialización es complicada para los que no somos expertos del tema!

创建者 Jorge C

•Sep 17, 2017

Sugerencia: Algunos de los ejemplos numéricos presentados en el curso podrían ir acompañados de alguna expresión matemática intermedia que facilite la comprensión de los mismos.

创建者 Christopher M P

•Jan 16, 2020

Simply excellent. A wonderful course to begin the representation of PGM. Be advised.... this can get quite advanced. It's all about that Bayes, 'bout that Bayes.... no trouble.

创建者 Christopher B

•Jul 17, 2017

learned a lot. lectures were easy to follow and the textbook was able to more fully explain things when I needed it. looking forward to the next course in the series.

创建者 Anthony L

•Jul 20, 2019

Some parts are challenging enough in the PAs, if you are familiar with Matlab this course is a great opportunity to get familiar with PGMs and learn to handle these.

创建者 ChrisLJ

•Mar 25, 2020

really great course! very clear and logical structure. I completed a graphical models course as part of my master's degree, and this really helped to consolidate it

创建者 Prasid S

•Dec 7, 2016

Very well designed. There were areas here I struggled with the technical details and had to read up a lot to understand. The assignments are very well designed.

创建者 Al F

•Mar 19, 2018

Excellent Course. Very Deep Material. I purchased the Text Book to allow for a deeper understanding and it made the course so much easier. Highly recommended