课程信息
96,734 次近期查看

第 3 门课程(共 7 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

高级

Course requires strong background in calculus, linear algebra, probability theory and machine learning.

完成时间大约为40 小时

建议:6 weeks of study, 6 hours/week...

英语(English)

字幕:英语(English), 韩语

您将获得的技能

Bayesian OptimizationGaussian ProcessMarkov Chain Monte Carlo (MCMC)Variational Bayesian Methods

第 3 门课程(共 7 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

高级

Course requires strong background in calculus, linear algebra, probability theory and machine learning.

完成时间大约为40 小时

建议:6 weeks of study, 6 hours/week...

英语(English)

字幕:英语(English), 韩语

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 2 小时

Introduction to Bayesian methods & Conjugate priors

9 个视频 (总计 55 分钟), 1 个阅读材料, 2 个测验
9 个视频
Bayesian approach to statistics5分钟
How to define a model3分钟
Example: thief & alarm11分钟
Linear regression10分钟
Analytical inference3分钟
Conjugate distributions2分钟
Example: Normal, precision5分钟
Example: Bernoulli4分钟
1 个阅读材料
MLE estimation of Gaussian mean10分钟
2 个练习
Introduction to Bayesian methods20分钟
Conjugate priors12分钟
2
完成时间为 6 小时

Expectation-Maximization algorithm

17 个视频 (总计 168 分钟), 3 个测验
17 个视频
Probabilistic clustering6分钟
Gaussian Mixture Model10分钟
Training GMM10分钟
Example of GMM training10分钟
Jensen's inequality & Kullback Leibler divergence9分钟
Expectation-Maximization algorithm10分钟
E-step details12分钟
M-step details6分钟
Example: EM for discrete mixture, E-step10分钟
Example: EM for discrete mixture, M-step12分钟
Summary of Expectation Maximization6分钟
General EM for GMM12分钟
K-means from probabilistic perspective9分钟
K-means, M-step7分钟
Probabilistic PCA13分钟
EM for Probabilistic PCA7分钟
2 个练习
EM algorithm8分钟
Latent Variable Models and EM algorithm10分钟
3
完成时间为 2 小时

Variational Inference & Latent Dirichlet Allocation

11 个视频 (总计 98 分钟), 2 个测验
11 个视频
Mean field approximation13分钟
Example: Ising model15分钟
Variational EM & Review5分钟
Topic modeling5分钟
Dirichlet distribution6分钟
Latent Dirichlet Allocation5分钟
LDA: E-step, theta11分钟
LDA: E-step, z8分钟
LDA: M-step & prediction13分钟
Extensions of LDA5分钟
2 个练习
Variational inference15分钟
Latent Dirichlet Allocation15分钟
4
完成时间为 5 小时

Markov chain Monte Carlo

11 个视频 (总计 122 分钟), 2 个测验
11 个视频
Sampling from 1-d distributions13分钟
Markov Chains13分钟
Gibbs sampling12分钟
Example of Gibbs sampling7分钟
Metropolis-Hastings8分钟
Metropolis-Hastings: choosing the critic8分钟
Example of Metropolis-Hastings9分钟
Markov Chain Monte Carlo summary8分钟
MCMC for LDA15分钟
Bayesian Neural Networks11分钟
1 个练习
Markov Chain Monte Carlo20分钟
4.6
117 个审阅Chevron Right

50%

完成这些课程后已开始新的职业生涯

36%

通过此课程获得实实在在的工作福利

来自Bayesian Methods for Machine Learning的热门评论

创建者 JGNov 18th 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.

创建者 LBJun 7th 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.

讲师

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Daniil Polykovskiy

Sr. Research Scientist
HSE Faculty of Computer Science
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Alexander Novikov

Researcher
HSE Faculty of Computer Science

关于 国立高等经济大学

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more. Learn more on www.hse.ru...

关于 高级机器学习 专项课程

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings....
高级机器学习

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  • Course requires strong background in calculus, linear algebra, probability theory and machine learning.

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