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287 Bewertungen

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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....

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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276 - 281 von 281 Bewertungen für Probabilistic Graphical Models 1: Representation

von Andrew M

24. Aug. 2020

The course content is solid. The honours content is challenging and interesting. There's a couple of minor glitches that cause frustration in the PA's but nothing too earth-shattering. There's a lot of whining and whinging on the message boards, but take it with a grain of salt: the instructions to succeed in the programing assignments are complete and relatively simple, but you might have to dig around in lecture transcripts to put all the puzzle pieces together. The is GRADUATE LEVEL work, don't expect to be spoon-fed, and don't whine when you're not. I'd recommend the content to anyone. SO WHY ONLY 1 STAR? Because there is absolutely no support from TAs or Mentors anywhere. Nada. Zero. Zilch. They are asleep at the switch. If you expect any kind of interaction to expand your learning horizon then you will be sorely disappointed. I sure was. The lack of engagement from the TA/Mentor community takes what could have been a 5 star experience and drops it to zero. But I can't go that low, so 1 star it is.

von aswin

10. Sep. 2020

Very rigid questions, very theoretical. Very poor instructor support. Content needs to be improved. Very disconnected approach.

von Ahmad C

11. Juni 2020

very shallow explanation of important concepts

von Jabberwoo

24. Juni 2017

Lectures are awful.

von Belal M

8. Sep. 2017

A very dry course.

von Francisco J G

4. Aug. 2020

Muy malo