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Kursteilnehmer-Bewertung und -Feedback für Bayesian Methods for Machine Learning von National Research University Higher School of Economics

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474 Bewertungen
128 Bewertungen

Über den Kurs

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine. When applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money. In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases be found with Bayesian methods. Do you have technical problems? Write to us: coursera@hse.ru...

Top-Bewertungen

JG

Nov 18, 2017

This course is little difficult. But I could find very helpful.\n\nAlso, I didn't find better course on Bayesian anywhere on the net. So I will recommend this if anyone wants to die into bayesian.

LB

Jun 07, 2019

Excellent course! The perfect balance of clear and relevant material and challenging but reasonable exercises. My only critique would be that one of the lecturers sounds very sleepy.

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76 - 100 von 122 Bewertungen für Bayesian Methods for Machine Learning

von François L

Mar 07, 2019

Very tough but very interesting

von Darwin D S P

Aug 10, 2018

the jupyter file is outdated

von Stephane H

Nov 13, 2019

Great course, quite hard.

von M A B

Jul 31, 2019

Amazing contents

von Harshit S

May 15, 2019

Awesome course !

von Navruzbek

Aug 17, 2018

Great course!!!!

von Amulya R B

Nov 05, 2017

Tough but a must

von Jue W

Apr 30, 2019

Very helpful!

von RITICK G

Dec 22, 2019

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von Goh

Jul 04, 2019

Excellent!

von Sankarshan M

Jul 09, 2019

very good

von Amrith S

May 17, 2018

Succinct!

von Kelvin L

May 25, 2018

Cool!!

von Dipanjan D

Jan 21, 2020

.

von Marcin L

Nov 14, 2019

The course was great, I've learned a lot about Bayesian perspective on Machine Learning. The level was satisfying, tasks and quizzes were demanding. It has been very interesting material to learn about.

I would give 5 stars, but eventually gave 4 because it had two drawbacks. First is, assignments are written in tensorflow v1, and occasionally there were issues with compatibility in some libraries. I don't know when the codes were last time refreshed, but unfortunately open source technologies tend to become deprecated very quickly, and the time has already affected course materials. Secondly, some of the most important derivations were made on blackboard, and are not included in downloadable slides. I would really like to keep them in some files, but the're not available.

Apart from these minor drawbacks, it's still a great course and definitely worth learning from.

von Mehrdad S

Sep 03, 2019

This is a great course for some of the advance topics in Baysian ML. The course starts off great and provides great explanation of the basic topics such as Conjugate, EM algorithm, etc. The related HW are also intelligently designed and fun to solve. But, as it reaches the weeks 5 and 6, things starts to fall apart and the materials are not presented and explained in the best possible way. I think the instructors try to teach many topics which requires a little bit of patience in a short amount of time. Overall, I believe its a course worthy of try, certainly provides great exposure to some of the advance topics but requires further follow ups and studies to completely digest all the materials.

von Bart-Jan V

Nov 23, 2018

Great course, great material, though difficult to follow a non native English speaker being non-english myself. Though the instructors know what they are talking about, they don't tell it in their own words but rather seem to have practiced their text.

Another important point is that it took me a lot of time to follow (pre)calculus and probability theory courses, to be able to understand this course. The course was a nice motivation to do that. I'm glad I did, because now I can understand and use VAE's and bayesian optimization (and some other useful stuff)

von Joris D

Jul 17, 2018

I can not recommend this course highly enough. Unfortunately I can't give it 5 stars since some of the computer assignments were outdated with respect to the tools they utilize (e.g. arguments in the assignments not existing anymore). Still, let that not discourage you. If you ever mentally disconnect when people start talking about Gibbs sampling, mean field approximations, intractable variational lower bounds, or other big fancy words, this is definitely the course for you. You'll discover that all these things are actually quite straightforward.

von Maury S

Aug 22, 2018

Excellent, detailed content for people wanting to understand variational methods for machine learning. Fairly high degree of math and statistics required as a prerequisite, as well as moderate ability as a Python programmer. Does not get 5 stars because some of the assignments had confusing instructions, and availability of instructors and others to asnwer questions was poor.

von Mauro D S

Jul 03, 2018

Hard material, but very well explained. The peer-review exercises are interesting as well, but if the reviewer does not understand the material, I wonder how useful they are. Open research question I guess (i.e. how to make sure the student reviewer understands what he is reviewing-are there any baseline reviews established a student should go through first?)!

von Milos V

Jan 08, 2019

As PhD in physics I found lecture super-boring (too much theory and derivation) and irrelevant to the practical assignment. On the other hand, most of practical assignments are explained very pedagogical manner (except week 5!). As for the first course - I would recommend more code-related lectures.

von Alexander E

Jun 02, 2018

Excellent material! I got new very useful knowledge. I really like the final project. Although course design is not perfect. It would be great to have additional content (links or documents), lectures are not enough to pass the tests. Also some assigments have issues (code and grader errors).

von 魏力

Jul 21, 2018

Good course. But some suggestions: topic about variational inference or variational EM in theory is quite tough, better to have equivalent level of assignment for better practical understanding. Personally, I feel VAE is a very simplified application case.

von 冯迪(Feng D

Feb 26, 2018

The materials of this lecture are awesome. Very useful! However, the introduction of project assignments are very confusing, especially the final project. It took me hours to understand what the task is really about, and what should we really do.

von Ishaan B

Nov 28, 2018

The content+course structure was phenomenal. The assignment environment setup was a bit cumbersome at times, but the level of difficulty in the assignments really solidified the understanding of the course material.