Retour à Bayesian Methods for Machine Learning

4.6

430 notes

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117 avis

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.

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par Daniel T

•Aug 06, 2019

The material is good and a lot of effort went into designing this course. Nonetheless, it feels neglected and could use an update.

The presentations are somewhat muddled by notational abuse. Indeed, it's customary to shorthand every distribution as "p" and let the arguments remind you which variable it came from, e.g, p(x|y) is conditional density of variable "X" at x given that "Y" = y. But then "p(a|b)" could be a completely different function corresponding to random variables "A" and "B"; however, you could have a=x and y=b as vectors which amplifies confusion... And when many variables with different ranges are involved and there's no consistency between labels for the variables and labels for their values, one has to spend extra time deciphering the material. Keeping track of the random variables and adopting a more suggestive notation would go a long way. Also, in Bayesian context it helps to avoid the word "parameter" (other than hyper-parameter, maybe), e.g., the weights "w" themselves are just values of a random variable, which is no different than the data generating process or the latent variables.

The programming assignments contain a lot of missing or inconsistent instructions. Be prepared to sift through the forums to find what is really expected or how to fix the issues in the supplied code.

Overall, I get the impression the course is now maintained by the students. It would be nice to see a revision from the instructors.

par hyunseung2 c

•Sep 19, 2019

ㅁㄴㄹ

par Ahmad

•Jan 16, 2019

Not structured well

par Gourab C

•Jun 26, 2018

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

par Luka N

•Nov 10, 2019

Too many probability concepts with too little examples and areas where one can apply them. Also, some steps in the computation are skipped which makes it harder for the learner to understand them. I spent hours trying to figure them out and get the result teachers have got on videos.

par 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.

par Jae L

•May 13, 2018

difficult to follow unstructured lecture contents.

par 张学立

•Nov 08, 2017

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

par 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.

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par Vadim K

•Sep 11, 2018

Terrible task design.

No PyMC documentation provided

par Dizhao J

•Aug 08, 2018

very bad Interpretation

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