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Kursteilnehmer-Bewertung und -Feedback für Bayesian Methods for Machine Learning von National Research University Higher School of Economics

417 Bewertungen
112 Bewertungen

Über den Kurs

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:



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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101 - 107 von 107 Bewertungen für Bayesian Methods for Machine Learning

von Gourab C

Jun 26, 2018

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

von Vadim K

Sep 11, 2018

Terrible task design.

No PyMC documentation provided

von Dizhao J

Aug 08, 2018

very bad Interpretation

von 张学立

Nov 08, 2017

it seems that the prof didn't prepare the course well

von Lizbeth R P

Jan 22, 2018

Maths are not easy but not impossible. However I find material not well prepared (defficient mathematical notation). Additionally, it takes a lot of time to get some help from the forums.

I encourage the instructors to revise the provided material.

von Jae L

May 13, 2018

difficult to follow unstructured lecture contents.

von Amith P

Oct 28, 2017

doesn't explain many of essential concepts / theories. This course is mainly for those who has graduate or post-graduate level knowledge of statistics, who ironically may not need this course.