Chevron Left
Retour à Probabilistic Graphical Models 1: Representation

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

4.7
étoiles
1,249 évaluations
274 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

ST

Jul 13, 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

Oct 23, 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).

Filtrer par :

226 - 250 sur 267 Avis pour Probabilistic Graphical Models 1: Representation

par Kevin W

Jan 17, 2017

The course is pretty good. I love the way that the professor led us into the graphical models.

par Péter D

Oct 29, 2017

great job, although the last PA is a huge pain / difficulty spike - more hints would be nice

par Andres P N

Jun 27, 2018

There are many error in the implementations for octave. Aside from that, the course is fine

par Ahmad E

Aug 20, 2017

Covers some material a little too quickly, but overall a good and entertaining course.

par Soteris S

Nov 27, 2017

A bit more challenging than I thought but very useful, and very well structured

par mathieu.zaradzki@gmail.com

Oct 04, 2016

Great and well paced content.

Quizzes really helps nailing the tricky points.

par Caio A M M

Dec 03, 2016

Instructor is engaging in her delivery. Topic is interesting but difficult.

par Michael B

Dec 12, 2019

Honors seems like a must to full instill concepts/implementation

par Anshuman S

May 08, 2019

I would recommend adding some supplemental reading material.

par Jhonatan d S O

May 25, 2017

Rich content and useful tools for applying in real problems

par Alberto C

Dec 01, 2017

Theory: Very interesting. Assignments: not so useful.

par Yuanduo H

Jan 20, 2020

Five stars minus the week 4 coding homework

par Arthur B

Jan 08, 2017

More feedback from TA would be appreciated

par Myoungsu C

Dec 26, 2018

Writing on the ppt is not clear to see.

par Soumyadipta D

Jul 16, 2019

lectures are too fast otherwise great

par Sunsik K

Jul 31, 2018

Broad introduction to general issues

par Tianyi X

Feb 20, 2018

Lack of top-down review of the PGM.

par Sunil

Sep 12, 2017

Great intro to probabilistic models

par Nikesh B

Nov 06, 2016

Excellent

par tyang16

Jun 20, 2019

too hard

par Yashwanth M

Jan 05, 2020

Good

par Paul C

Oct 31, 2016

I found plenty of useful information in this course overall but lectures often spent too much time dwelling on the detail of simpler concepts while more complex areas, and sometimes critical information that was later built upon, were only touched briefly or sometimes skipped entirely. I missed a sense of continuity as we skipped from model to model with a minimum of time spent on how the models complement each other and their relative strengths and weaknesses in application.

The way data structures were defined in the code was particularly difficult to deal with. The coding exercises all suffered as a result. It ended up taking way too much time to figure how to decode the data and trace logic around it. This meant that grasping concepts and learning from the questions came in a distant second priority to debugging.

Dr Koller mentioned that the material is aimed at postgraduates. I felt that the level of content covered here would just as easily be grasped by most undergraduates in technical disciplines if it had been delivered in a more structured manner with clearer progression across models (conceptually and mathematically) and better code examples. When delivering in this format, allowances need to be made for the facts that tutorial sessions do not exist and the possibilities for informal Q&A are limited so any gaps become very difficult for students to fill in themselves.

Despite the above shortcomings I'm glad I did the course and I would still recommend it to someone interested in graphical models as it does cover the basics well enough to make a decent start. I'm not sure whether or not I'd recommend the programming exercises as they are a significant time sink but at the same time, without spending time attacking the programming problems the concepts are not likely to gel based on the video and quizzes alone.

par Nicholas E

Oct 29, 2016

The course was very interesting and thought-provoking. I found the introduction to probabilistic graphical models (PGMs) and their properties struck a nice balance between intuition and formalism. The discussions highlighted exciting aspects of their power in simplifying complex problems involving uncertainty. However, I still do not feel I could propose convincing PGMs for real-world problems. There are examples in the course, but they are far removed from being concrete applications. I would have preferred there be an in depth analysis of an application of PGMs in the literature over the lengthy programming assignments. I am an experienced programmer with over 5 years of experience in many languages including MATLAB/Octave and I sometimes found it uninspiring to solve toy problems, not due to the difficulty in using the programming language, but rather because after the assignment had been completed I felt I had not really learnt much more than I would have from just watching the lectures, although, if you are interested in getting experience with MATLAB/Octave, the programming assignments are good practice. I qualify this in stating that I have not yet completed the next two courses on PGMs; this course may present an essential foundation that is necessary for the upcoming courses, and in any case provoked my interest in learning more about them

par Mahendra K

Oct 04, 2017

The course is highly theoretical. Would have been great if it was paced well and driven from real world examples. I am not saying that there are no examples. But it'd have been better if the concepts were driven via some real world examples instead of first talking about the concept and then its applications.

What would have been even better if Python was an option for PAs. Octave can't be used in industry setting where the amount of data is really large. Both Python and Octave should have been an option so that the student can decide for themself.

par John E M

Apr 01, 2018

Lectures were OK and quizzes and exams appropriately difficult. But Labs were pretty difficult especially lab 4 which I ended up surrendering on. This means I didn't do the accompanying quiz and gave up on the possibility of honors recognition as well.

While labs don't have to be as hand-holding as the DeepLearning class by Coursera, it would be nice to get more help and maybe not submit errors for the parts I haven't tackled yet when submitting (as DeepLearning and MachineLearning courses figured out how to do).