課程概述

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...

2017年11月17日

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.

2019年6月6日

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.

學生對国立高等经济大学提供的 Bayesian Methods for Machine Learning 的評價和反饋

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101 - Bayesian Methods for Machine Learning 的 125 個評論（共 175 個）

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學生對 国立高等经济大学 提供的 Bayesian Methods for Machine Learning 的評價和反饋

課程概述

熱門審閱

101 - Bayesian Methods for Machine Learning 的 125 個評論（共 175 個）

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熱門在線課程

熱門在線專項課程

在線證書

在線學位課程

Coursera

社區

更多

學生對国立高等经济大学提供的 Bayesian Methods for Machine Learning 的評價和反饋