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返回到 Probabilistic Graphical Models 1: Representation

學生對 斯坦福大学 提供的 Probabilistic Graphical Models 1: Representation 的評價和反饋

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1,381 個評分

課程概述

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....

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ST

2017年7月12日

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

2017年10月22日

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 - Probabilistic Graphical Models 1: Representation 的 150 個評論(共 301 個)

創建者 CIST N

2019年10月30日

Good way to learn Probabilistic Graphical Models in practical

創建者 Prazzy S

2018年1月20日

Challenging! Regret not doing the coding assignment for honors

創建者 Gautam B

2017年7月4日

Great course loved the ongoing feedback when doing the quizes.

創建者 Achen

2018年5月6日

a bit too hard if you don't have enough probability knowledge

創建者 albert b

2017年11月4日

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

創建者 Arthur C

2017年6月4日

Super useful if you want to understand any probability model.

創建者 Ruiliang L

2021年2月15日

Awesome class to gain solid understanding of graphical model

創建者 Phong V

2020年3月18日

Great course, learned a lots. Thanks professor Daphne Koller

創建者 Sriram P

2017年6月24日

Had a wonderful learning experience, Thank You Daphne Ma'am.

創建者 Pablo G M D

2018年7月18日

Outstanding teaching and the assignments are quite useful!

創建者 Henry H

2016年11月14日

Very informative course, and incredibly useful in research

創建者 Ingyo J

2018年10月4日

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

創建者 Renjith K A

2018年9月23日

Was really helpful in understanding graphic models

創建者 Roger T

2017年3月5日

very challenging class but very rewarding as well!

創建者 Harshit A

2021年4月20日

This is a challenging but very satisfying course.

創建者 吕野

2016年12月26日

Good course lectures and programming assignments

創建者 Mahmoud S

2019年2月25日

Very good explanation and excellent assignments

創建者 Lilli B

2018年2月2日

Brilliant content and charismatic lecturer!!!

創建者 Fabio S

2017年9月25日

Excellent, well structured, clear and concise

創建者 Orlando D

2017年7月19日

Very good and excellent course and assignment

創建者 Parag S

2019年8月14日

Learn the basic things in probability theory

創建者 Christian S

2020年12月11日

Highest level in coursera courses so far.

創建者 Jonathan H

2017年11月25日

This course is hard and very interesting!

創建者 Shengliang X

2017年5月29日

excellent explanations! Thanks professor!

創建者 Alexander K

2017年5月16日

Thank you for all. This is gift for us.