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學生對 国立高等经济大学 提供的 Bayesian Methods for Machine Learning 的評價和反饋

4.5
574 個評分
163 條評論

課程概述

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...

熱門審閱

JG

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.

LB

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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151 - Bayesian Methods for Machine Learning 的 157 個評論(共 157 個)

創建者 Beibit

Jun 27, 2019

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

創建者 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。

創建者 hyunseung2 c

Sep 19, 2019

ㅁㄴㄹ

創建者 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.

創建者 Gourab C

Jun 26, 2018

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

創建者 Ahmad

Jan 16, 2019

Not structured well

創建者 Hamed G

Jul 31, 2020

Very poor explanation of the theory and math.