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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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176 - 195 sur 195 Avis pour Bayesian Methods for Machine Learning

par Gereon F

10 sept. 2020

I like the course content and the presentations in general. I have two major points bothering me though which is why i "only" gave 3 stars:

1.The code is old. It is written in old versions of the libraries and it silly studying with those.

2. There were many mathematical formulas that were thrown around. Without graphical or real world examples i felt like it was hard to follow sometimes. One could argue that the listener has to have this knowledge going into the course but i still think that some different kinds of examples would have helped.

To conclude with something positive i must really admit that i had little clue of the background of various Bayesian ML Methods before. Know i have a good overview and was able to even program some of the Methods myself. I feel like it really enhanced my ML knowledge.

par Deepanshu J

2 nov. 2020

The course teaches advanced concept from Bayesian Machine Learning which are often not easily found on most of the online courses. The approach is math heavy but I believe adding some more intuition could have improved the course a bit. My major disappointment from this otherwise decent course was the quality of assignments. The programming assignments were often poorly worded with little hint about what is expected. I often had to look thorugh forum to understand what is expected to be done. Often times there were bugs or library issues in the assignment and again one was left looking for updated API notes for modules or debugging weird issues on StackOverflow.

par Tim v d B

22 déc. 2019

The first exercises are sessions are fun and very good.

However, the last exercise is a catastrophy. Conflicting instructions. Once I should upload a HTML version but nobody says who. Then suddenly the rules are changed and it is supposed to upload it some google cloud. This platform is qute annoying. Either I cannot edit my work any more or suddenly it just disappears. The editor is also very bad. This is just unfair. Really the technical problems in the final project are too extreme.

par Espoir M

19 janv. 2022

The good aspect of the course , I like how it cover theorical aspect of machine learning in deep.

But it is now outdated , the course need some updates, a lot have been done in the deep learning field since the release of the course.

The exercices could have been more challenging.

par AJay 2

14 déc. 2020

The course gets very technical at times, skimming over some important aspects. Multiple watches required to understand some of the videos. Otherwise, great selection of content and concepts

par Kapil d

2 déc. 2020

Lecturers are reading formulae from slides ! need to improve presentation. Focus on building intuition. Upgrade programming assignment versions. Still have to use tensor 1.0


11 nov. 2020

Interesting topics but very superficially explained. Simple theory lessons but very complex tests with many libraries that you have to learn by yourself.

par Lavinia T

29 janv. 2018

The trainer's English is not very good, and the explanations provided are insufficient.

par Beibit

27 juin 2019

As the description suggests this course is very advanced and math heavy.

par Siwei Y

20 févr. 2018

给三星是因为所选的 TOPICS 很好, 真的很好。但是,说到老师的讲解,就真的不敢恭维了。从逻辑性到流畅性都让人捏把汗啊。希望改进。

par hyunseung2 c

18 sept. 2019


par Alexander P

10 mars 2020

The instructions are hard to follow. Most of the material presented as purely mathematical derivation exercises that do not have stated goal.

On the plus side the topics covered in the course are very interesting. Personally, I ended up using this course as a guide and looked for explanations elsewhere.

par Gourab C

26 juin 2018

I felt the explanations too mechanical and in between they skipped a lot of concepts and explanations.

par Ahmad

16 janv. 2019

Not structured well

par Ralph K

22 mars 2021

The material is presented in a very abstract way which often makes it difficult to follow. The programming exercises are also difficult, if not impossible, to complete only with the material presented in the course.

par Hazem A

2 janv. 2021

Good strong content and assignments , however the language fluency of the instructors is a BIG barrier ... I can hardly understand the speech utterances of the instructors ..

par Hamed G

31 juil. 2020

Very poor explanation of the theory and math.

par Pavel A

17 oct. 2020

Lecturer doesn't explain anything.

par Sheril A T

25 févr. 2021

The teaching style is very bad.

par Meenali S

13 oct. 2021

Very difficult to understand-