Chevron Left
Retour à Probabilistic Graphical Models 3: Learning

Avis et commentaires pour d'étudiants pour Probabilistic Graphical Models 3: Learning par Université de Stanford

296 évaluations

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 third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem....

Meilleurs avis


11 oct. 2020

An amazing course! The assignments and quizzes can be insanely difficult espceially towards the conclusion.. Requires textbook reading and relistening to lectures to gather the nuances.


29 janv. 2018

very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.

Filtrer par :

1 - 25 sur 52 Avis pour Probabilistic Graphical Models 3: Learning

par Akshaya T

14 mars 2019

par Amine M

17 juin 2019

par Ahmed S

22 sept. 2017

par Maxim V

30 avr. 2020

par Rohan M

5 déc. 2019

par Dat N

14 nov. 2019

par Lik M C

23 févr. 2019

par Shawn

20 août 2020

par Diogo P

15 nov. 2017

par Rishabh G

3 juin 2020

par Jesus I G R

30 mai 2020

par Michel S

14 juil. 2018

par mgbacher

6 mars 2021

par Antônio H R

6 nov. 2018

par Sergey S

24 sept. 2020

par Alfred D

13 août 2020

par Shi Y

19 janv. 2019

par Chan-Se-Yeun

21 févr. 2018

par Rishi C

4 juin 2018

par Musalula S

25 août 2018

par Joseph W

9 janv. 2020

par Jaime A C

15 nov. 2022

par Satish P

12 oct. 2020

par Orlando D

30 janv. 2018

par Henry H

13 févr. 2017