Probabilistic Graphical Models 1: Representation(으)로 돌아가기

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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....

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!!

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).

필터링 기준:

교육 기관: Arjun V

•Dec 04, 2016

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

교육 기관: Ka L K

•Mar 27, 2017

A five stars course. Prof. Koller is an outstanding scientists in this field. The first part just introduce you two basic frames of graphical models. So go further into second part is necessary if you want to have a bigger picture. The whole course is an introduction to the book - Probabilistic Graphical Models of Prof. Koller, so buying her book is also highly recommended. This course is supposed to be hard, so you should expect a steep learning curve. But all the efforts you made are worthy. I suggest coursera will consider put more challenging exercises in order to extent the concentration. Finally, a highly respect to Prof. Koller who provide the course in such a theoretical depth.

교육 기관: Chuck M

•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).

교육 기관: Phung H X

•Oct 30, 2016

very good course

교육 기관: Youwei Z

•May 20, 2018

Very informative. The only drawback is lack of rigorous proof and clear definition summaries.

교육 기관: Alireza N

•Jan 12, 2017

Excellent!

교육 기관: Fabio S

•Sep 25, 2017

Excellent, well structured, clear and concise

교육 기관: Abhishek K

•Nov 06, 2016

Difficult yet very good to understand even after knowing about ML for a long time.

교육 기관: Gautam K

•Oct 17, 2016

This course probably the only best of class course available online. Prof Daphne Koller is one of the very few authority on this subject. I am glad to sign up this course and after completing gave me a great satisfaction learning Graphical Model. I also purchased the book written by Prof. Koller and Prof Friedman and I am going to continue my study on this subject.

교육 기관: Al F

•Mar 20, 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

교육 기관: Nguyễn L T Â

•Feb 06, 2018

Thank you, the professor.

교육 기관: oilover

•Dec 03, 2016

老师很棒！！

교육 기관: Johannes C

•Mar 08, 2018

necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.

교육 기관: Pedro R

•Nov 09, 2016

great course

교육 기관: Phan T B

•Dec 02, 2016

very good!

교육 기관: Ziheng

•Nov 14, 2016

Very informative course, and incredibly useful in research

교육 기관: Prasid S

•Dec 08, 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.

교육 기관: Venkateshwaralu

•Oct 26, 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!

교육 기관: albert b

•Nov 04, 2017

Best course anywhere on this topic. Plus Daphne is the best !

교육 기관: Hao G

•Nov 01, 2016

Awesome course! I feel like bayesian method is also very useful for inference in daily life.

교육 기관: 吕野

•Dec 26, 2016

Good course lectures and programming assignments

교육 기관: 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!

교육 기관: 王文君

•May 21, 2017

Awesome class, the content is not too easy as most online courses. Still the instructor states the concepts clearly and the assignments aligns very well with the content to help me deepen my understanding of the concepts. The assignments are meaningful and challenging, finishing them gave me a great sense of achievement!!

It would be better if the examples in the classes could incorporate some industry applications.

교육 기관: llv23

•Jul 19, 2017

Very good and excellent course and assignment

교육 기관: Souvik C

•Oct 26, 2016

Extremely helpful course