75,875 次近期查看

#### 高級

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

### 您將獲得的技能

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

#### 高級

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

1

## 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.

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9 個視頻 （總計 55 分鐘）, 1 個閱讀材料, 2 個測驗
9 個視頻
Bayesian approach to statistics5分鐘
How to define a model3分鐘
Example: thief & alarm11分鐘
Linear regression10分鐘
Analytical inference3分鐘
Conjugate distributions2分鐘
Example: Normal, precision5分鐘
Example: Bernoulli4分鐘
1 個閱讀材料
MLE estimation of Gaussian mean10分鐘
2 個練習
Introduction to Bayesian methods20分鐘
Conjugate priors12分鐘
2

## 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.

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17 個視頻 （總計 168 分鐘）, 3 個測驗
17 個視頻
Probabilistic clustering6分鐘
Gaussian Mixture Model10分鐘
Training GMM10分鐘
Example of GMM training10分鐘
Jensen's inequality & Kullback Leibler divergence9分鐘
Expectation-Maximization algorithm10分鐘
E-step details12分鐘
M-step details6分鐘
Example: EM for discrete mixture, E-step10分鐘
Example: EM for discrete mixture, M-step12分鐘
Summary of Expectation Maximization6分鐘
General EM for GMM12分鐘
K-means from probabilistic perspective9分鐘
K-means, M-step7分鐘
Probabilistic PCA13分鐘
EM for Probabilistic PCA7分鐘
2 個練習
EM algorithm8分鐘
Latent Variable Models and EM algorithm10分鐘
3

## 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

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11 個視頻 （總計 98 分鐘）, 2 個測驗
11 個視頻
Mean field approximation13分鐘
Example: Ising model15分鐘
Variational EM & Review5分鐘
Topic modeling5分鐘
Dirichlet distribution6分鐘
Latent Dirichlet Allocation5分鐘
LDA: E-step, theta11分鐘
LDA: E-step, z8分鐘
LDA: M-step & prediction13分鐘
Extensions of LDA5分鐘
2 個練習
Variational inference15分鐘
Latent Dirichlet Allocation15分鐘
4

## 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.

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11 個視頻 （總計 122 分鐘）, 2 個測驗
11 個視頻
Sampling from 1-d distributions13分鐘
Markov Chains13分鐘
Gibbs sampling12分鐘
Example of Gibbs sampling7分鐘
Metropolis-Hastings8分鐘
Metropolis-Hastings: choosing the critic8分鐘
Example of Metropolis-Hastings9分鐘
Markov Chain Monte Carlo summary8分鐘
MCMC for LDA15分鐘
Bayesian Neural Networks11分鐘
1 個練習
Markov Chain Monte Carlo20分鐘
4.6
95 個審閱

## 36%

### 來自Bayesian Methods for Machine Learning的熱門評論

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.

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.

## 講師

### Daniil Polykovskiy

Researcher
HSE Faculty of Computer Science

### Alexander Novikov

Researcher
HSE Faculty of Computer Science

## 關於 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...

## 關於 高级机器学习 專項課程

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....

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