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.
Este curso faz parte do Programa de cursos integrados Aprendizagem de máquina avançada
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Programa de cursos integrados Aprendizagem de máquina avançadaUniversidade HSEInformações sobre o curso
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Resultados de carreira do aprendiz
38%
comecei uma nova carreira após concluir estes cursos
24%
consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível avançado
Course requires strong background in calculus, linear algebra, probability theory and machine learning.
Aprox. 33 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
Habilidades que você terá
Bayesian OptimizationGaussian ProcessMarkov Chain Monte Carlo (MCMC)Variational Bayesian Methods
Resultados de carreira do aprendiz
38%
comecei uma nova carreira após concluir estes cursos
24%
consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível avançado
Course requires strong background in calculus, linear algebra, probability theory and machine learning.
Aprox. 33 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
oferecido por
Universidade HSE
HSE University 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.
Programa - O que você aprenderá com este curso
3 horas para concluir
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.
3 horas para concluir
10 vídeos (Total 57 mín.), 3 leituras, 2 testes
10 videos
Think bayesian & Statistics review7min
Bayesian approach to statistics5min
How to define a model3min
Example: thief & alarm11min
Linear regression10min
Analytical inference3min
Conjugate distributions2min
Example: Normal, precision5min
Example: Bernoulli4min
3 leituras
About the University10min
Rules on the academic integrity in the course10min
MLE estimation of Gaussian mean10min
2 exercícios práticos
Introduction to Bayesian methods30min
Conjugate priors30min
7 horas para concluir
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.
7 horas para concluir
17 vídeos (Total 168 mín.)
17 videos
Probabilistic clustering6min
Gaussian Mixture Model10min
Training GMM10min
Example of GMM training10min
Jensen's inequality & Kullback Leibler divergence9min
Expectation-Maximization algorithm10min
E-step details12min
M-step details6min
Example: EM for discrete mixture, E-step10min
Example: EM for discrete mixture, M-step12min
Summary of Expectation Maximization6min
General EM for GMM12min
K-means from probabilistic perspective9min
K-means, M-step7min
Probabilistic PCA13min
EM for Probabilistic PCA7min
2 exercícios práticos
EM algorithm30min
Latent Variable Models and EM algorithm30min
2 horas para concluir
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
2 horas para concluir
11 vídeos (Total 98 mín.)
11 videos
Mean field approximation13min
Example: Ising model15min
Variational EM & Review5min
Topic modeling5min
Dirichlet distribution6min
Latent Dirichlet Allocation5min
LDA: E-step, theta11min
LDA: E-step, z8min
LDA: M-step & prediction13min
Extensions of LDA5min
2 exercícios práticos
Variational inference15min
Latent Dirichlet Allocation15min
6 horas para concluir
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.
6 horas para concluir
11 vídeos (Total 122 mín.)
11 videos
Sampling from 1-d distributions13min
Markov Chains13min
Gibbs sampling12min
Example of Gibbs sampling7min
Metropolis-Hastings8min
Metropolis-Hastings: choosing the critic8min
Example of Metropolis-Hastings9min
Markov Chain Monte Carlo summary8min
MCMC for LDA15min
Bayesian Neural Networks11min
1 exercício prático
Markov Chain Monte Carlo30min
Avaliações
Principais avaliações do BAYESIAN METHODS FOR MACHINE LEARNING
por JG17 de Nov de 2017
This course is little difficult. But I could find very helpful. Also, I didn't find better course on Bayesian anywhere on the net. So I will recommend this if anyone wants to die into bayesian.
por HR18 de Jun de 2018
It probably offers the most comprehensive overview of Bayesian methods online. However, it would be nice these methods translate into practical data science problems found in the industry.
por LB6 de Jun de 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.
por C29 de Jan de 2020
The course covers a lot of very advanced material and is a great starting point for Bayesian Methods, but it would greatly benefit from having additional reading materials.
Sobre Programa de cursos integrados Aprendizagem de máquina avançada
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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