学生对 Stanford University 提供的 Probabilistic Graphical Models 1: Representation 的评价和反馈
课程概述
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SC
Nov 4, 2016
The course is great with plenty of knowledge. A little defect is about description about assignment. As the forum discussed, several quizzes may confusing.
CC
Mar 24, 2020
really great course! very clear and logical structure. I completed a graphical models course as part of my master's degree, and this really helped to consolidate it
251 - Probabilistic Graphical Models 1: Representation 的 275 个评论(共 314 个)
创建者 Boxiao M
•Jun 28, 2017
The lecture was a bit too compact and unsystematic. However, if you also do a lot of reading of the textbook, you can learn a lot. Besides, the Quiz and Programming task are of high qualities.
创建者 Yiting T
•Oct 15, 2022
Top notch course! I only wish the explanations for answer choices in the quizzes/exams were more elaborate, as some of them are single sentences that don't really provide justification.
创建者 Shawn C
•Nov 4, 2016
The course is great with plenty of knowledge. A little defect is about description about assignment. As the forum discussed, several quizzes may confusing.
创建者 Shane C
•May 18, 2020
concepts in the videos are well presented. additional readings from the textbook are helpful to cement concepts not explained as thoroughly in the videos
创建者 Hilmi E
•Feb 16, 2020
I really enjoyed attending this course. It is foundational material for anyone who wants to use graphical models for inference and decision making..
创建者 Nimo F B
•Sep 10, 2020
Great content and easy to pick up. Only issue was with downloaded Octave software. Does not work, despite multiple downloads on different machines
创建者 Roman S
•Mar 20, 2018
A good introduction to PGM, from very basic concepts to some move in-depth features. A big disadvantage is Matlab/Octave programming assignments.
创建者 serge s
•Oct 18, 2016
Thanks to this course, Probabilistic Graphical Models are not anymore an esoteric subject! I am really looking for the second part of the course.
创建者 Jack A
•Nov 5, 2017
The class was very exciting and challenging, but I felt the programming assignments weren't dependent on understanding the classwork at all.
创建者 Francois L
•Mar 16, 2020
Really interesting contents but it would be great to have the exercises in a more up to date programming environment (python for instance)
创建者 Gorazd H R
•Jul 7, 2018
A very demanding course with some glitches in lectures and materials. The topic itself is very interesting, educational and useful.
创建者 Ashwin P
•Jan 9, 2017
Great material. Course mentors are nowhere to be found and some of the problems are hard, so I'd have liked to see some guidance.
创建者 Forest R
•Feb 20, 2018
Excellent introduction into probabilistic graph models. Introduced me to Baysian analysis and is quite helpful for my work.
创建者 Иван М
•Apr 26, 2020
Great course, would be nicer if exercises were in Python or R and if software from first honours task worked on Mac.
创建者 Victor Z
•Dec 22, 2018
Some interesting knowledges about PCM, but I think I need more detailed information in the succeeding courses.
创建者 Luiz C
•Jun 25, 2018
Good course, quite complex, wish some better quality slides, and more quizzes to help understand the theory
创建者 Saurabh N
•Mar 23, 2020
The coding assignments can be compulsory too.
Maybe not as vast, but maybe interleaved with the quizzes
创建者 Werner N
•Dec 28, 2016
Very good course. It should contain more practical examples to make the material better to understand.
创建者 Haitham S S S
•Nov 24, 2016
Great course, however, the honors track assignments are a bit too tedious and take lots of time.
创建者 Kevin W
•Jan 17, 2017
The course is pretty good. I love the way that the professor led us into the graphical models.
创建者 Péter D
•Oct 29, 2017
great job, although the last PA is a huge pain / difficulty spike - more hints would be nice
创建者 Andres P N
•Jun 26, 2018
There are many error in the implementations for octave. Aside from that, the course is fine
创建者 Ahmad E
•Aug 19, 2017
Covers some material a little too quickly, but overall a good and entertaining course.
创建者 Soteris S
•Nov 27, 2017
A bit more challenging than I thought but very useful, and very well structured
Great and well paced content.
Quizzes really helps nailing the tricky points.