返回到 Bayesian Methods for Machine Learning

4.6

403 個評分

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106 個審閱

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...

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.

篩選依據：

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 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 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 Navruzbek

•Aug 17, 2018

Great course!!!!

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 Jue W

•Apr 30, 2019

Very helpful!

創建者 Igor B

•Apr 18, 2019

A wonderful course to improve the theoretical understanding of machine learning and recap probability theory. The lecturers did their best to drag the listener through the math of the EM algorithm and more. The transition to Google Colab indeed simplified online work with Jupyter notebooks.

創建者 Gary

•May 03, 2019

Covered many important points in the course.

創建者 Ануфриев С С

•Apr 07, 2019

So far the most interesting course in specialisation

創建者 Harshit S

•May 15, 2019

Awesome course !

創建者 Xinyue W

•May 24, 2019

Fantastic contents! It explains a lot of concepts that confused me when I started Bayesian machine learning very well.

創建者 Tirth P

•Jun 11, 2019

Mathematically Heavy and highly theoretical course. This makes this course unique and awesome

創建者 Goh

•Jul 04, 2019

Excellent!

創建者 Sankarshan M

•Jul 09, 2019

very good

創建者 Murat Ö

•Jul 23, 2019

A great course to learn probabilistic machine learning!

創建者 M A B

•Jul 31, 2019

Amazing contents

創建者 Parag H S

•Aug 14, 2019

Bayesian Methods for machine learning course was great

創建者 Debasis S

•Aug 23, 2019

I found it tuff to get everything, but a very good course

創建者 Ayush T

•Aug 24, 2019

It is undoubtedly one of the best course on Coursera that I've come across. This is really well taught and there is a good balance between the theoretical and the practical aspect of the Bayesian Machine Learning. This course is must-do for those who want to do some good projects in the field of Bayesian Deep Learning which is currently a hot topic now.

創建者 Truong D

•Sep 04, 2019

Easy way to approach the Probability

創建者 Bart-Jan V

•Nov 23, 2018

Great course, great material, though difficult to follow a non native English speaker being non-english myself. Though the instructors know what they are talking about, they don't tell it in their own words but rather seem to have practiced their text.

Another important point is that it took me a lot of time to follow (pre)calculus and probability theory courses, to be able to understand this course. The course was a nice motivation to do that. I'm glad I did, because now I can understand and use VAE's and bayesian optimization (and some other useful stuff)

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 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.