Retour à Bayesian Methods for Machine Learning

4.6

425 notes

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114 avis

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...

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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par Hythem S

•Dec 16, 2017

Excellent course with great theoretical and practical coverage. There aren't many online courses that offer in-depth coverage of Bayesian methods. Keep in mind this is a newer course and there are a few kinks that still need to be ironed out, but the issues are minor.

par Bob F

•Mar 11, 2018

This class provided excellent lectures and very instructive programming assignments. I don't think that the material covered is available in any other MOOC. This class is among the very best I've taken, which is saying a lot because they have to compete with Andrew Ng, Geoff Hinton, and Chris Manning - just to mention few! Thanks for all the great work!

par Chan H Y

•May 08, 2018

This course requires fairly good mathematics background. Some topics cover in this course are not often being taught (or only taught in advance research courses) in Computer Science or Engineering Department in other Universities

par Shingo M

•Jul 07, 2018

this course is very hard for me.but helpful

par Navruzbek

•Aug 17, 2018

Great course!!!!

par Vaibhav O

•Apr 03, 2019

Great introduction to Bayesian methods, with quite good hands on assignments. This course will definitely be the first step towards a rigorous study of the field.

par Jue W

•Apr 30, 2019

Very helpful!

par Igor B

•Apr 18, 2019

A wonderful course to improve the theoretical understanding of machine learning and recap probability theory. The lecturers did their best to drag the listener through the math of the EM algorithm and more. The transition to Google Colab indeed simplified online work with Jupyter notebooks.

par Gary

•May 03, 2019

Covered many important points in the course.

par Ануфриев С С

•Apr 07, 2019

So far the most interesting course in specialisation

par Harshit S

•May 15, 2019

Awesome course !

par Xinyue W

•May 24, 2019

Fantastic contents! It explains a lot of concepts that confused me when I started Bayesian machine learning very well.

par Tirth P

•Jun 11, 2019

Mathematically Heavy and highly theoretical course. This makes this course unique and awesome

par Goh

•Jul 04, 2019

Excellent!

par Sankarshan M

•Jul 09, 2019

very good

par Murat Ö

•Jul 23, 2019

A great course to learn probabilistic machine learning!

par M A B

•Jul 31, 2019

Amazing contents

par Parag H S

•Aug 14, 2019

Bayesian Methods for machine learning course was great

par Debasis S

•Aug 23, 2019

I found it tuff to get everything, but a very good course

par Ayush T

•Aug 24, 2019

It is undoubtedly one of the best course on Coursera that I've come across. This is really well taught and there is a good balance between the theoretical and the practical aspect of the Bayesian Machine Learning. This course is must-do for those who want to do some good projects in the field of Bayesian Deep Learning which is currently a hot topic now.

par Atul K

•Nov 27, 2017

Excellent content, we need more advanced courses like this. Assignments are also very interesting.

par Truong D

•Sep 04, 2019

Easy way to approach the Probability

par Akhil K

•Oct 04, 2019

Very comprehensive & touched upon some very interesting problems!

par Igor P

•Oct 09, 2019

Excellent course. Definitely touches advanced topics with the due rigor.

par Alya S

•Oct 29, 2019

Very well structured and delivered course. The explanations are generally easy to follow and reproduce. Highly enjoyable and instructive. Assignments are relevant. It would have been great to have an assignment for the Dirichlet Allocation this would have improved the overall understanding of the algorithm. Overall very satisfied I took this lecture. Thanks very much to the lecturers.

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