Вернуться к Bayesian Methods for Machine Learning

4.6

Оценки: 234

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Рецензии: 72

Bayesian methods are used in lots of fields: 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 really desirable feature for fields like medicine.
When Bayesian methods are applied to deep learning, it turns out that they allow you to compress your models 100 folds, and automatically tune hyperparametrs, 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 fully 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....

автор: 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.

автор: AE

•May 09, 2018

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

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Рецензии: 70

автор: Anmol Gupta

•Dec 06, 2018

One of the best in-depth course.

автор: Zixu Zhang

•Dec 02, 2018

Course content is excellent. However I hope it could have had more about MCMC. That part was pretty thin.

автор: Ishaan Bhat

•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.

автор: Bart-Jan Verhoeff

•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)

автор: Alexander Riley

•Nov 12, 2018

super helpful and very applicable!

автор: Голубев Кирилл Олегович

•Oct 19, 2018

Great course with fine lecturers and deep immersion in Bayesian methods

автор: Dongxiao Zhao

•Oct 11, 2018

Learned a lot from this course. Thanks!

автор: SagarSrinivas

•Sep 29, 2018

Awesome. Worth it!

автор: Peter Kostovčík

•Sep 25, 2018

This course was really good - it started from easy things for beginners and ended with awesome aplication of bayesian neural networks. Since I have masters in Probability and Statistics I was familiar with most of the stuff and I must thank you fot the mathematics and some proofs. It's hard to find such nice math proofs in today's courses, so it is good for non-mathematicians to the science behind these methods.

Most of the lectures were quite good and for beginner who is willing to study many stuff himself it is good. But I must say that some quizes had questions which answers you couldn't find in the lectures. I recommend to add some more reading stuff mainly for beginners.

автор: Yanting Huang

•Sep 18, 2018

A very detailed course for someone who wants to strengthen their statistical background.

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