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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
403 個評分
106 個審閱

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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26 - Bayesian Methods for Machine Learning 的 50 個評論(共 102 個)

創建者 RLee

Feb 15, 2019

The only solid online course on Bayesian ML methods!

創建者 Jordi W

Feb 28, 2019

This is a challenging course, but well worth it! One needs to be able to manage both the lecture content and the practical side of the course, namely the Python modules/environment. The Python ecosystem is developping fast and some modules changed since the assignments have been created. This means that you need to be able handle deprecations within Python modules and your own Python environment if needed. But this is an advanced course, so I think that is fine. Things have been made easier now that the course creaters have moved assignments to Colaboratory.

創建者 Vaibhav O

Apr 03, 2019

Great introduction to Bayesian methods, with quite good hands on assignments. This course will definitely be the first step towards a rigorous study of the field.

創建者 Ануфриев С С

Apr 07, 2019

So far the most interesting course in specialisation

創建者 François L

Mar 07, 2019

Very tough but very interesting

創建者 Erwin P

Mar 17, 2019

This course provides a comprehensive overview how Bayes stats can be used in ML. I'm better able to value the different concepts like EM, GP and VAE and put them into perspective. Depending on you previous math and stats skills the assignments can be challenging and it took me some stamina to complete. The "Russian English" is sometimes a bit of a hurdle when watching the videos, but you get used to it. The concepts are well explained and the references to the additional materials useful.

創建者 Darwin D S P

Aug 10, 2018

the jupyter file is outdated

創建者 Navruzbek

Aug 17, 2018

Great course!!!!

創建者 David G

Aug 21, 2018

A very good course with lots of challenging but interesting content. Prior knowledge of Statistics and ML is highly recommended or essential prior to starting the course because there is a steep learning curve.

創建者 Meng-Chieh L

Sep 05, 2018

This is a very interesting class and I learned some concepts and techniques that are beneficial to my work as a data scientist.

創建者 Amulya R B

Nov 05, 2017

Tough but a must

創建者 ilya.a.kazakov

May 12, 2018

Great work the creators of the course did!

創建者 Alex E

May 09, 2018

Challenging, but well designed course covering cutting edge ML methods. The course assumes high proficency with Tensorflow, Keras, and Python.

創建者 Chan H Y

May 08, 2018

This course requires fairly good mathematics background. Some topics cover in this course are not often being taught (or only taught in advance research courses) in Computer Science or Engineering Department in other Universities

創建者 Shingo M

Jul 07, 2018

this course is very hard for me.but helpful

創建者 Atul K

Nov 27, 2017

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

創建者 yan l

Mar 06, 2018

The lecture in real detail explain what is going on behind the model!

創建者 Trinadh

Jun 29, 2018

great enlightener into bayesian view of deeplearning

創建者 Diogo P

Jan 05, 2018

Great course. The material is explained with great detail, including the respective mathematical proofs. The assignments could be a bit more demanding, though. The instructors support is very good - they usually answer every question in the forum in a few days.

創建者 Liu Y

Mar 17, 2018

Concise but very informative, challenges not only from knowledge but also from various tools if you've never met them before. Indeed, great course!

創建者 Sun X

Jun 13, 2018

Excellent course! I really learned a lot about Bayesian methods, especially EM algorithm, Variational Inference, VAE, but still did not understand LDA, Bayesian optimization well. It will be better to introduce some backgrounds. Thanks for the lecturers!

創建者 Савельев Н

Dec 11, 2017

Best CS-related course on coursera yet

創建者 Hythem S

Dec 16, 2017

Excellent course with great theoretical and practical coverage. There aren't many online courses that offer in-depth coverage of Bayesian methods. Keep in mind this is a newer course and there are a few kinks that still need to be ironed out, but the issues are minor.

創建者 Bob F

Mar 11, 2018

This class provided excellent lectures and very instructive programming assignments. I don't think that the material covered is available in any other MOOC. This class is among the very best I've taken, which is saying a lot because they have to compete with Andrew Ng, Geoff Hinton, and Chris Manning - just to mention few! Thanks for all the great work!

創建者 Martin K

Mar 16, 2018

The course material is very well prepared and self-contained. Derivation of relevant mathematical formulas is done in great detail which was really helpful. If you've read books like Murphy's "Machine Learning - A Probabilistic Perspective" or Bishop's "Pattern Recognition and Machine Learning" then this course should be easy to follow. If not, it is helpful to have one of these books at hand to get a better understanding, as some topics are presented in a rather condensed form. Thanks to the lecturers for preparing this great course. I can highly recommend it!