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413 Bewertungen

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111 Bewertungen

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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von Sanjay K

•Jan 26, 2018

Fantastic lecturer.. very crisp and informative

von John A D

•Mar 13, 2018

Excellent course ! Pointed to concepts and techniques that would be hard to access without this expert guidance.

von Kelvin L

•May 25, 2018

Cool!!

von Subhamoy B

•May 20, 2018

I would like to thank the instructors for this great course. This is definitely not an easy course. But the learning has been immense.

von Nimish S

•Dec 31, 2017

The first and best indepth course on Bayesian methods.

von Alex

•Mar 01, 2018

Excellent course. Nice work, lectors.

Interesting approach with reverted video and glass wall for formulas inference.

von Arjun B

•Nov 25, 2017

Excellent content, quite challenging.

von Gary

•May 03, 2019

Covered many important points in the course.

von 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.

von Xinyue W

•May 24, 2019

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

von Jue W

•Apr 30, 2019

Very helpful!

von Harshit S

•May 15, 2019

Awesome course !

von Tirth P

•Jun 11, 2019

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

von Murat Ö

•Jul 23, 2019

A great course to learn probabilistic machine learning!

von M A B

•Jul 31, 2019

Amazing contents

von Parag H S

•Aug 14, 2019

Bayesian Methods for machine learning course was great

von 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.

von Debasis S

•Aug 23, 2019

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

von Goh

•Jul 04, 2019

Excellent!

von Sankarshan M

•Jul 09, 2019

very good

von Truong D

•Sep 04, 2019

Easy way to approach the Probability

von Atul K

•Nov 27, 2017

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

von Akhil K

•Oct 04, 2019

Very comprehensive & touched upon some very interesting problems!

von Alya S

•Oct 07, 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 about 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.

von Igor P

•Oct 09, 2019

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

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