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

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474 Bewertungen
128 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: coursera@hse.ru...

Top-Bewertungen

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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101 - 123 von 123 Bewertungen für Bayesian Methods for Machine Learning

von 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.

von 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 !

von 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.

von 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.

von 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.

von 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.

von 洪贤斌

Aug 30, 2018

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

von MASSON

Apr 06, 2019

Good course.

Too much theory, not enough practice

von 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.

von 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.

von 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.

von Lavinia T

Jan 29, 2018

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

von Beibit

Jun 27, 2019

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

von 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。

von hyunseung2 c

Sep 19, 2019

ㅁㄴㄹ

von Gourab C

Jun 26, 2018

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

von Ahmad

Jan 16, 2019

Not structured 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 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.

von 张学立

Nov 08, 2017

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

von Vadim K

Sep 11, 2018

Terrible task design.

No PyMC documentation provided

von Jae L

May 13, 2018

difficult to follow unstructured lecture contents.

von Dizhao J

Aug 08, 2018

very bad Interpretation