Bayesian Methods for Machine Learning

开始时间: 04/22/2022 持续时间: Unknown

所在平台: CourseraArchive

课程类别: 计算机科学

大学或机构: CourseraNew




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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:

贝叶斯机器学习方法:人们在许多领域应用贝叶斯方法:从游戏开发到药物发现。它们为许多机器学习算法提供了超能力:处理丢失的数据,从小型数据集中提取更多信息。贝叶斯方法还使我们能够估计预测中的不确定性,这是医学等领域的理想功能。 当应用于深度学习时,贝叶斯方法使您可以将模型压缩一百倍,并自动调整超参数,从而节省时间和金钱。 在六周的时间里,我们将讨论贝叶斯方法的基础知识:从如何定义概率模型到如何从中进行预测。我们将看到如何使这一工作流程自动化,以及如何使用一些先进的技术来加快它的速度。 我们还将看到贝叶斯方法在深度学习中的应用以及如何使用它来生成新图像。我们将看到如何使用贝叶斯方法找到治疗严重疾病的新药。 你有技术上的问题吗?写信给我们


Welcome to first week of our course! Today we will discuss what bayesian methods are and what are probabilistic models. We will see how they can be used to model real-life situations and how to make conclusions from them. We will also learn about conjugate priors — a class of models where all math becomes really simple.





机器学习 贝叶斯方法 面向机器学习的贝叶斯方法 机器学习中的贝叶斯方法 贝叶斯 深度学习 数据 缺失数据 小数据 调参 预测 压缩