Chevron Left
Zurück zu Bayesian Methods for Machine Learning

Kursteilnehmer-Bewertung und -Feedback für Bayesian Methods for Machine Learning von National Research University Higher School of Economics

4.6
Sterne
474 Bewertungen
128 Bewertungen

Über den Kurs

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...

Top-Bewertungen

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.

LB

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.

Filtern nach:

51 - 75 von 122 Bewertungen für Bayesian Methods for Machine Learning

von Alex

Mar 01, 2018

Excellent course. Nice work, lectors.

Interesting approach with reverted video and glass wall for formulas inference.

von Roshan C

Dec 04, 2019

The best course to learn about maths behind the optimization and also helped to clarify the mathematics lies behind

von John A D

Mar 13, 2018

Excellent course ! Pointed to concepts and techniques that would be hard to access without this expert guidance.

von Atul K

Nov 27, 2017

Excellent content, we need more advanced courses like this. Assignments are also very interesting.

von Tirth P

Jun 11, 2019

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

von Husain B

Dec 09, 2019

Amazing course, take you from brief to advance level concepts of bayesian stats and methods.

von Kiran K R

Dec 31, 2019

This is the best introductory course that is available for Bayesian deep learning.

von Igor P

Oct 09, 2019

Excellent course. Definitely touches advanced topics with the due rigor.

von yan l

Mar 06, 2018

The lecture in real detail explain what is going on behind the model!

von Akhil K

Oct 04, 2019

Very comprehensive & touched upon some very interesting problems!

von Debasis S

Aug 23, 2019

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

von Murat Ö

Jul 23, 2019

A great course to learn probabilistic machine learning!

von Parag H S

Aug 14, 2019

Bayesian Methods for machine learning course was great

von Nimish S

Dec 31, 2017

The first and best indepth course on Bayesian methods.

von Ануфриев С С

Apr 07, 2019

So far the most interesting course in specialisation

von RLee

Feb 15, 2019

The only solid online course on Bayesian ML methods!

von Trinadh

Jun 29, 2018

great enlightener into bayesian view of deeplearning

von Sanjay K

Jan 26, 2018

Fantastic lecturer.. very crisp and informative

von Gary

May 03, 2019

Covered many important points in the course.

von Shingo M

Jul 07, 2018

this course is very hard for me.but helpful

von ilya.a.kazakov

May 12, 2018

Great work the creators of the course did!

von Chen Z

Feb 11, 2020

Its a really good course, I learnt a lot

von Савельев Н

Dec 11, 2017

Best CS-related course on coursera yet

von Arjun B

Nov 25, 2017

Excellent content, quite challenging.

von Truong D

Sep 04, 2019

Easy way to approach the Probability