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

4.5
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549 Bewertungen
159 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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101 - 125 von 153 Bewertungen für Bayesian Methods for Machine Learning

von Савельев Н

Dec 11, 2017

Best CS-related course on coursera yet

von Arjun B

Nov 25, 2017

Excellent content, quite challenging.

von Truong D

Sep 04, 2019

Easy way to approach the Probability

von Oscar J P V

Mar 05, 2020

Very complete course. Recommended!

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the jupyter file is outdated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 vania j t

May 21, 2020

excellent course!

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 Xiaoying W

May 07, 2020

Thanks a lot!

von Jue W

Apr 30, 2019

Very helpful!

von RITICK G

Dec 22, 2019

gfhgngfbfv

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 Hrushikesh L

Jul 02, 2020

gOOD

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 Raffael S

May 15, 2020

This course is very good. However, the weeks on Variational Methods and Gaussian Processes need more detail or references to extra reading material as they don't very much into depth. Also, a few theoretical exercises would have been nice. E.g. calculating a simple example with non-conjugate priors. Finally, I feel like the notebooks could do with a major update to TF 2.0 and Keras as well as GPy. I spend a few hours chasing non-existent bugs in my code when the problem was that the solutions changed numerically from one version to the other and you have to find out which one.

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)