课程信息
4.6
247 个评分
75 个审阅
专项课程

第 3 门课程(共 7 门)

100% 在线

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

可灵活调整截止日期

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高级

高级

完成时间(小时)

完成时间大约为40 小时

建议:6 weeks of study, 6 hours/week...
可选语言

英语(English)

字幕:英语(English)

您将获得的技能

Bayesian OptimizationGaussian ProcessMarkov Chain Monte Carlo (MCMC)Variational Bayesian Methods
专项课程

第 3 门课程(共 7 门)

100% 在线

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

可灵活调整截止日期

根据您的日程表重置截止日期。
高级

高级

完成时间(小时)

完成时间大约为40 小时

建议:6 weeks of study, 6 hours/week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 2 小时

Introduction to Bayesian methods & Conjugate priors

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....
Reading
9 个视频 (总计 55 分钟), 1 个阅读材料, 2 个测验
Video9 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
Reading1 个阅读材料
MLE estimation of Gaussian mean10分钟
Quiz2 个练习
Introduction to Bayesian methods20分钟
Conjugate priors12分钟
2
完成时间(小时)
完成时间为 7 小时

Expectation-Maximization algorithm

This week we will about the central topic in probabilistic modeling: the Latent Variable Models and how to train them, namely the Expectation Maximization algorithm. We will see models for clustering and dimensionality reduction where Expectation Maximization algorithm can be applied as is. In the following weeks, we will spend weeks 3, 4, and 5 discussing numerous extensions to this algorithm to make it work for more complicated models and scale to large datasets....
Reading
17 个视频 (总计 168 分钟), 3 个测验
Video17 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
Quiz2 个练习
EM algorithm8分钟
Latent Variable Models and EM algorithm10分钟
3
完成时间(小时)
完成时间为 2 小时

Variational Inference & Latent Dirichlet Allocation

This week we will move on to approximate inference methods. We will see why we care about approximating distributions and see variational inference — one of the most powerful methods for this task. We will also see mean-field approximation in details. And apply it to text-mining algorithm called Latent Dirichlet Allocation...
Reading
11 个视频 (总计 98 分钟), 2 个测验
Video11 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
Quiz2 个练习
Variational inference15分钟
Latent Dirichlet Allocation15分钟
4
完成时间(小时)
完成时间为 5 小时

Markov chain Monte Carlo

This week we will learn how to approximate training and inference with sampling and how to sample from complicated distributions. This will allow us to build simple method to deal with LDA and with Bayesian Neural Networks — Neural Networks which weights are random variables themselves and instead of training (finding the best value for the weights) we will sample from the posterior distributions on weights....
Reading
11 个视频 (总计 122 分钟), 2 个测验
Video11 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
Quiz1 个练习
Markov Chain Monte Carlo20分钟
4.6
75 个审阅Chevron Right
职业方向

60%

完成这些课程后已开始新的职业生涯
工作福利

44%

通过此课程获得实实在在的工作福利
职业晋升

11%

加薪或升职

热门审阅

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 AEMay 9th 2018

Challenging, but well designed course covering cutting edge ML methods. The course assumes high proficency with Tensorflow, Keras, and Python.

讲师

Avatar

Daniil Polykovskiy

Researcher
HSE Faculty of Computer Science
Avatar

Alexander Novikov

Researcher
HSE Faculty of Computer Science

关于 National Research University Higher School of Economics

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 communications, IT, mathematics, engineering, and more. Learn more on www.hse.ru...

关于 Advanced Machine Learning 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....
Advanced Machine Learning

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

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