Retour à Bayesian Methods for Machine Learning

4.6

327 notes

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91 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....

par 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.

par VO

•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.

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87 avis

par Harshit Sharma

•May 15, 2019

Awesome course !

par Gary

•May 03, 2019

Covered many important points in the course.

par Jue Wang

•Apr 30, 2019

Very helpful!

par Igor Buzhinskii

•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.

par Ануфриев Сергей Сергеевич

•Apr 07, 2019

So far the most interesting course in specialisation

par MASSON

•Apr 06, 2019

Good course.

Too much theory, not enough practice

par Kuldeep Jiwani

•Apr 04, 2019

Various advanced Machine Learning topics like Bayesian interpretation techniques, probabilistic modelling, variational auto encoders, etc. have been explained in a very intuitive and simple manner. Then the assignments are well designed to make sure one is able to work on the existing packages available.

par Vaibhav Ojha

•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.

par Karishma Dixit

•Mar 25, 2019

Lots of maths! :). Assignments were very interesting as well.

But overall, this has been my favourite course so far. I like how in depth the lectures went into the maths (made me feel like I was back at uni). However, if I did not have a maths + stats background (from university), I think I would have struggled to keep up with the content

Couple of comments though:

1) For the MCMC week, it would have helped my understanding if we had to fit a Bayesian model to a dataset from scratch via our own implementation of Metropolis Hastings for example in addition to using the pymc3 library.

2) For the Gaussian Processes week, it would have helped my understanding if we had to fit a GP to some data via our own implementation in addition to using the GPy library.

par Maciej

•Mar 24, 2019

Overall it's good. My problem is that most of this material is better suited to lecture notes and not a video. They're forcing it into a video since it's coursera. Couldn't get through a lot of the lectures, used a textbook instead.

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