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Retour à Probabilistic Graphical Models 1: Representation

Avis et commentaires pour d'étudiants pour Probabilistic Graphical Models 1: Representation par Université de Stanford

1,313 évaluations
292 avis

À propos du cours

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....

Meilleurs avis

12 juil. 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!!

22 oct. 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 - 285 sur 285 Avis pour Probabilistic Graphical Models 1: Representation

par Michel S

14 juil. 2018

Good course, but the material really needs a refresh!

par Robert M

6 févr. 2018

Started off well. Finished poorly

par Peter

29 sept. 2016

The content seems to be excellent regarding "what" is presented. But sadly the sound quality is rather bad: Sounds like an age-old valve radio with A LOT of dropouts. And Professor Daphne is an agile and therefore less disciplined speaker which lessens the understandability of her speech in conjunction with the poor sound quality furthermore. Especially for me as a non-native foreign english speaker it is very hard to follow. And now I am at one point in the course, that is "Flow of Probalistic Influence", where she explains a concept without explaining what is meant with the used underlying notions "flow" and "influence" which makes me difficult to understand what is going on. That means in my point of view that the slides are not sufficiently prepared. Although I'm very interested in the topic I am asking myself after the first view videos if I should continue or drop because my cognitive capacitity is for me to worthful to use it for the decoding of badly prepared and presented material. Ok, my decision heuristic in such cases is "Use the hammer not the tweezers!". Therefore I have dropped. Please improve the state of this class from beta to release. Then I will come back.

par Jennifer H

15 déc. 2019

Quite abstract. A solid mathematical grounding, but largely devoid of practicalities. Optional exercises are quite basic, and don't get to the heart of the matter. Lectures are confusing, as undefined terminology come up out of the blue, and key concepts aren't clearly explained.

par Andrew M

24 août 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.

par aswin

10 sept. 2020

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

par Ahmad C

11 juin 2020

very shallow explanation of important concepts

par Jabberwoo

24 juin 2017

Lectures are awful.

par Belal M

8 sept. 2017

A very dry course.

par Francisco J G

4 août 2020

Muy malo