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Avis et commentaires pour d'étudiants pour Bayesian Methods for Machine Learning par Université nationale de recherche, École des hautes études en sciences économiques

528 évaluations
155 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 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:

Meilleurs avis


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.


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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126 - 149 sur 149 Avis pour Bayesian Methods for Machine Learning

par 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.

par 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.

par Guy K

Mar 19, 2018

a very important material is covered in a clear manner.

some of the labs could have been more effective (e.g. avoid unnecessary mixing between tensorflow and Keras)

Strongly recommended course ! great curriculum !

par Hugo R C R

Jun 19, 2018

It probably offers the most comprehensive overview of Bayesian methods online. However, it would be nice these methods translate into practical data science problems found in the industry.

par P C

Jan 30, 2020

The course covers a lot of very advanced material and is a great starting point for Bayesian Methods, but it would greatly benefit from having additional reading materials.

par Olaf W

Jun 26, 2018

Great class. Well presented material. Sometimes the path from introduction to advanced material could use a few steps in between.

par Diego E P M

Apr 10, 2020

A very good course on Bayesian methods, though I find explanations are a bit confusing from time to time.

par Chiang y

Jun 04, 2018

We may need more help for homework format or quiz answer format. It took me lots time for solving it.

par Maxim V

Mar 27, 2020

Amazing course, an absolute must! Only some programming assignments were having minor issues.

par Sai H Y

May 17, 2020

covering Additional and recent Bayesian methods will make this course exceptional

par 洪贤斌

Aug 30, 2018

Good course but a bit difficult and the peer review is helpless


Apr 06, 2019

Good course.

Too much theory, not enough practice

par Biarnès A

Apr 18, 2020

This course is pretty challenging in the sens that one really has to put some effort into understanding the materials and completing the programming assinments. But the problem with this course is the level of english of the speakers which is not that high and also the pedagogical aspects. The teachers should put more time into explainings the models and their details. They should also try to rephrase several times or explain things with different angles. It really goes too fast

par Tim v d B

Dec 22, 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 Pengchong L

Aug 28, 2018

Not very well prepared. Contents are dry and not well illustrated. Failed to explain points that are made in the videos. The lecturers are reading from scripts and look very nervous.

par Artem E

Jun 03, 2018

Not so good as I thought. Some times is too complicated and dry. Need more balance. I hope, that guys can better. But I want to say thanks to authors. You did a great job! Good luck.

par Aviv B

Mar 18, 2020

Explanations are very technical and do not develop any intuition as to what the methods are supposed to accomplish.

par Lavinia T

Jan 29, 2018

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

par Beibit

Jun 27, 2019

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

par Siwei Y

Feb 20, 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

Sep 19, 2019


par Alexander P

Mar 10, 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

Jun 26, 2018

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

par Ahmad

Jan 16, 2019

Not structured well