À propos de ce cours
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Niveau avancé

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

Approx. 39 heures pour terminer

Recommandé : 6 weeks of study, 6 hours/week...


Sous-titres : Anglais, Coréen

Compétences que vous acquerrez

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

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau avancé

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

Approx. 39 heures pour terminer

Recommandé : 6 weeks of study, 6 hours/week...


Sous-titres : Anglais, Coréen

Programme du cours : ce que vous apprendrez dans ce cours

2 heures pour terminer

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.

9 vidéos (Total 55 min), 1 lecture, 2 quiz
9 vidéos
Bayesian approach to statistics5 min
How to define a model3 min
Example: thief & alarm11 min
Linear regression10 min
Analytical inference3 min
Conjugate distributions2 min
Example: Normal, precision5 min
Example: Bernoulli4 min
1 lecture
MLE estimation of Gaussian mean10 min
2 exercices pour s'entraîner
Introduction to Bayesian methods20 min
Conjugate priors12 min
6 heures pour terminer

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.

17 vidéos (Total 168 min), 3 quiz
17 vidéos
Probabilistic clustering6 min
Gaussian Mixture Model10 min
Training GMM10 min
Example of GMM training10 min
Jensen's inequality & Kullback Leibler divergence9 min
Expectation-Maximization algorithm10 min
E-step details12 min
M-step details6 min
Example: EM for discrete mixture, E-step10 min
Example: EM for discrete mixture, M-step12 min
Summary of Expectation Maximization6 min
General EM for GMM12 min
K-means from probabilistic perspective9 min
K-means, M-step7 min
Probabilistic PCA13 min
EM for Probabilistic PCA7 min
2 exercices pour s'entraîner
EM algorithm8 min
Latent Variable Models and EM algorithm10 min
2 heures pour terminer

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

11 vidéos (Total 98 min), 2 quiz
11 vidéos
Mean field approximation13 min
Example: Ising model15 min
Variational EM & Review5 min
Topic modeling5 min
Dirichlet distribution6 min
Latent Dirichlet Allocation5 min
LDA: E-step, theta11 min
LDA: E-step, z8 min
LDA: M-step & prediction13 min
Extensions of LDA5 min
2 exercices pour s'entraîner
Variational inference15 min
Latent Dirichlet Allocation15 min
5 heures pour terminer

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.

11 vidéos (Total 122 min), 2 quiz
11 vidéos
Sampling from 1-d distributions13 min
Markov Chains13 min
Gibbs sampling12 min
Example of Gibbs sampling7 min
Metropolis-Hastings8 min
Metropolis-Hastings: choosing the critic8 min
Example of Metropolis-Hastings9 min
Markov Chain Monte Carlo summary8 min
MCMC for LDA15 min
Bayesian Neural Networks11 min
1 exercice pour s'entraîner
Markov Chain Monte Carlo20 min
5 heures pour terminer

Variational Autoencoder

Welcome to the fifth week of the course! This week we will combine many ideas from the previous weeks and add some new to build Variational Autoencoder -- a model that can learn a distribution over structured data (like photographs or molecules) and then sample new data points from the learned distribution, hallucinating new photographs of non-existing people. We will also the same techniques to Bayesian Neural Networks and will see how this can greatly compress the weights of the network without reducing the accuracy.

10 vidéos (Total 79 min), 3 lectures, 3 quiz
10 vidéos
Modeling a distribution of images10 min
Using CNNs with a mixture of Gaussians8 min
Scaling variational EM15 min
Gradient of decoder6 min
Log derivative trick6 min
Reparameterization trick7 min
Learning with priors5 min
Dropout as Bayesian procedure5 min
Sparse variational dropout5 min
3 lectures
VAE paper10 min
Relevant papers10 min
Categorical Reparametrization with Gumbel-Softmax10 min
2 exercices pour s'entraîner
Variational autoencoders16 min
Categorical Reparametrization with Gumbel-Softmax18 min
4 heures pour terminer

Gaussian processes & Bayesian optimization

Welcome to the final week of our course! This time we will see nonparametric Bayesian methods. Specifically, we will learn about Gaussian processes and their application to Bayesian optimization that allows one to perform optimization for scenarios in which each function evaluation is very expensive: oil probe, drug discovery and neural network architecture tuning.

7 vidéos (Total 58 min), 2 quiz
7 vidéos
Gaussian processes8 min
GP for machine learning5 min
Derivation of main formula11 min
Nuances of GP12 min
Bayesian optimization10 min
Applications of Bayesian optimization5 min
1 exercice pour s'entraîner
Gaussian Processes and Bayesian Optimization16 min
5 heures pour terminer

Final project

In this module you will apply methods that you learned in this course to this final project

1 quiz
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Principaux examens pour Bayesian Methods for Machine Learning

par 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.

par VOApr 3rd 2019

Great introduction to Bayesian methods, with quite good hands on assignments. This course will definitely be the first step towards a rigorous study of the field.



Daniil Polykovskiy

HSE Faculty of Computer Science

Alexander Novikov

HSE Faculty of Computer Science

À propos de Université nationale de recherche, École des hautes études en sciences économiques

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...

À propos de la Spécialisation Apprentissage automatique avancé

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....
Apprentissage automatique avancé

Foire Aux Questions

  • Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.

  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

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

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