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Über den Kurs

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....

Top-Bewertungen

ST
12. Juli 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. Okt. 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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126 - 150 von 286 Bewertungen für Probabilistic Graphical Models 1: Representation

von Pablo G M D

18. Juli 2018

Outstanding teaching and the assignments are quite useful!

von Ziheng

14. Nov. 2016

Very informative course, and incredibly useful in research

von Ingyo C

4. Okt. 2018

What a wonderful course that I haven't ever taken before.

von Renjith K A

23. Sep. 2018

Was really helpful in understanding graphic models

von Roger T

5. März 2017

very challenging class but very rewarding as well!

von 吕野

26. Dez. 2016

Good course lectures and programming assignments

von Mahmoud S

25. Feb. 2019

Very good explanation and excellent assignments

von Lilli B

2. Feb. 2018

Brilliant content and charismatic lecturer!!!

von Fabio S

25. Sep. 2017

Excellent, well structured, clear and concise

von llv23

19. Juli 2017

Very good and excellent course and assignment

von Parag H S

14. Aug. 2019

Learn the basic things in probability theory

von Christian S

11. Dez. 2020

Highest level in coursera courses so far.

von Jonathan H

25. Nov. 2017

This course is hard and very interesting!

von Shengliang X

29. Mai 2017

excellent explanations! Thanks professor!

von Alexander K

16. Mai 2017

Thank you for all. This is gift for us.

von Chahat C

4. Mai 2019

lectures not good(i mean not detailed)

von Harshdeep S

19. Juli 2019

Excellent blend of maths & intuition.

von NARENDRAN

7. März 2020

Very good explanation on the subject

von Jui-wen L

20. Juni 2019

Easy to follow and very informative.

von Miriam F

27. Aug. 2017

Very nice and well prepared course!

von Gary H

27. März 2018

Great instructor and information.

von Subham S

28. Apr. 2020

I enjoyed the course very much!

von George S

18. Juni 2017

Excellent material presentation

von 郭玮

25. Apr. 2019

Really nice course, thank you!

von hyesung J

10. Okt. 2019

So difficult. But interesting