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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

4.6
492 個評分
137 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...

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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 - Bayesian Methods for Machine Learning 的 125 個評論(共 131 個)

創建者 Milos V

Jan 08, 2019

As PhD in physics I found lecture super-boring (too much theory and derivation) and irrelevant to the practical assignment. On the other hand, most of practical assignments are explained very pedagogical manner (except week 5!). As for the first course - I would recommend more code-related lectures.

創建者 Alexander E

Jun 02, 2018

Excellent material! I got new very useful knowledge. I really like the final project. Although course design is not perfect. It would be great to have additional content (links or documents), lectures are not enough to pass the tests. Also some assigments have issues (code and grader errors).

創建者 魏力

Jul 21, 2018

Good course. But some suggestions: topic about variational inference or variational EM in theory is quite tough, better to have equivalent level of assignment for better practical understanding. Personally, I feel VAE is a very simplified application case.

創建者 冯迪(Feng D

Feb 26, 2018

The materials of this lecture are awesome. Very useful! However, the introduction of project assignments are very confusing, especially the final project. It took me hours to understand what the task is really about, and what should we really do.

創建者 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.

創建者 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 !

創建者 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.

創建者 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.

創建者 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.

創建者 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.

創建者 Maxim V

Mar 27, 2020

Amazing course, an absolute must! Only some programming assignments were having minor issues.

創建者 洪贤斌

Aug 30, 2018

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

創建者 MASSON

Apr 06, 2019

Good course.

Too much theory, not enough practice

創建者 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.

創建者 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.

創建者 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.

創建者 Aviv B

Mar 18, 2020

Explanations are very technical and do not develop any intuition as to what the methods are supposed to accomplish.

創建者 Lavinia T

Jan 29, 2018

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

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 Michael G

Mar 13, 2020

It is a great idea for a course -- very important in today's ML environment.

However, I felt the instructors did not give much of the "big picture" reasons for why they were teaching each individual technical detail. As a result, I know some more math, but not much about how to apply it to ML. I'm going to have to go online and independently read materials available on the subject so I can better internalize this and figure out how to use it for my purposes in ML.

Although I admire the instructors for giving the class in what is obviously not their first language, it was still quite difficult to follow sometimes when words were mumbled or mispronounced. This could be improved if someone technical could review the lecture transcripts and fill in all the errors and [INAUDIBLE] notices.

The programming assignments were OK, but mostly struggling with syntax rather than concepts. The python package GPyOpt that we used has awful documentation, so we were in effect blindly applying some process optimization code to our homework, without any idea of what it was doing to it and how we could adjust the parameters to better suit our particular application.

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