Bayesian Methods for Machine Learning に戻る

4.6

(331 件の評価)

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....

by 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.

by 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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88件のレビュー

by Xinyue Wang

•May 24, 2019

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

by Harshit Sharma

•May 15, 2019

Awesome course !

by Gary

•May 03, 2019

Covered many important points in the course.

by Jue Wang

•Apr 30, 2019

Very helpful!

by 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.

by Ануфриев Сергей Сергеевич

•Apr 07, 2019

So far the most interesting course in specialisation

by MASSON

•Apr 06, 2019

Good course.

Too much theory, not enough practice

by 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.

by 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.

by 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.