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Avis et commentaires pour d'étudiants pour Bayesian Methods for Machine Learning par Université HSE

684 évaluations
201 avis

À propos du cours

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 this online HSE course 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 can be found with Bayesian methods. Do you have technical problems? Write to us:

Meilleurs avis

17 nov. 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.

6 juin 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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126 - 150 sur 196 Avis pour Bayesian Methods for Machine Learning

par Navruzbek K

17 août 2018

Great course!!!!

par Amulya R B

5 nov. 2017

Tough but a must

par stephane d

3 déc. 2021

Great course!

par MD A R A

27 août 2020

Excellent !!!

par Xiaoying W

7 mai 2020

Thanks a lot!

par Ada W

29 avr. 2019

Very helpful!

par Tianyi Z

18 août 2020



22 déc. 2019


par Goh

4 juil. 2019


par Sankarshan M

9 juil. 2019

very good

par Amrith S

17 mai 2018


par Kelvin L

25 mai 2018


par Juan J P G

30 mars 2021


par Hrushikesh L

2 juil. 2020


par Dipanjan D

21 janv. 2020


par Marcin L

14 nov. 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.

par Andrea S

27 mars 2021

The course is good and cover all the important topics in Bayesian machine learning. However, even after passing it with a 98% score, I am not sure I would have the confidence to think out of the box (and out of the course exercises) to tackle any real life machine learning problem using the Bayesian approach. I am happy I covered all the basics, and I can now read papers having a better clue what they're talking about, but by just doing the course you probably won't reach that level of critical thinking necessary to enable you to apply these concepts to completely new areas and problems. I guess it's acceptable considering it's only a 6 weeks course, but be aware of this. I am speaking from being a Machine Learning Engineer myself and implementing non-Bayesian ML algorithms (especially in the field of NLP and deep learning) on a daily basis in my job.

par Erik B

22 juil. 2020

The material that was discussed was quite interesting. In particular, the variational inference, varfational auto encoders, and Gaussion process optimization.

I found the course material a bit lacking tough. There was one lecture where the main formulas for Guassian inference were derived that had a huge mistake in it. Also, sometimes, the assignments were totally unclear. The final project was a bit demotivating. Given the low quality of the pre-trained VAE, the results are not that good. If a pre-trained VAE was used, then why not simply use a bigger pre-trained VAE? Also, the final assignment was really easy and the whole peer review process felt a bit over the top. I must have spent more time in the forums getting my work reviewed than working on the final project.

par Mehrdad S

3 sept. 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.

par Raffael S

15 mai 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.

par Bart-Jan V

23 nov. 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)

par Joris D

17 juil. 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.

par Pallavi J

11 juin 2020

This course covered everything I wanted to learn about Bayesian approaches to machine learning. Also, quizes are informative as in if I select something wrong, the valuable feedback is given as in why this option should not be selected which clearly shows that course creators just do not want to make the quizes complex but also want students to learn through those quizes.

Thanks for the course!

par Saptashwa B

4 mars 2020

Fantastic course! Very comprehensive introduction to Bayesian analysis. There's though room for improvement from what I have experienced. One suggestion I have is to provide transcript written and checked by the lecturers and not some auto generated script! This would help us a lot better to understand the video and won't mislead us, which at times the transcript really does!!

par Maury S

22 août 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.