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 forma parte de Programa Especializado - Aprendizaje automático avanzado
ofrecido por
Programa Especializado - Aprendizaje automático avanzadoNational Research University Higher School of EconomicsAcerca de este Curso
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Resultados profesionales del estudiante
38%
comenzó una nueva carrera después de completar estos cursos
24%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 3 de 7 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel avanzado
Course requires strong background in calculus, linear algebra, probability theory and machine learning.
Aprox. 33 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish)
Habilidades que obtendrás
Bayesian OptimizationGaussian ProcessMarkov Chain Monte Carlo (MCMC)Variational Bayesian Methods
Resultados profesionales del estudiante
38%
comenzó una nueva carrera después de completar estos cursos
24%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 3 de 7 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel avanzado
Course requires strong background in calculus, linear algebra, probability theory and machine learning.
Aprox. 33 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish)
ofrecido por
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 communicamathematics, engineering, and more.
Programa - Qué aprenderás en este curso
3 horas para completar
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 completar
10 videos (Total 57 minutos), 3 lecturas, 2 cuestionarios
10 videos
Think bayesian & Statistics review7m
Bayesian approach to statistics5m
How to define a model3m
Example: thief & alarm11m
Linear regression10m
Analytical inference3m
Conjugate distributions2m
Example: Normal, precision5m
Example: Bernoulli4m
3 lecturas
About the University10m
Rules on the academic integrity in the course10m
MLE estimation of Gaussian mean10m
2 ejercicios de práctica
Introduction to Bayesian methods30m
Conjugate priors30m
7 horas para completar
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 completar
17 videos (Total 168 minutos)
17 videos
Probabilistic clustering6m
Gaussian Mixture Model10m
Training GMM10m
Example of GMM training10m
Jensen's inequality & Kullback Leibler divergence9m
Expectation-Maximization algorithm10m
E-step details12m
M-step details6m
Example: EM for discrete mixture, E-step10m
Example: EM for discrete mixture, M-step12m
Summary of Expectation Maximization6m
General EM for GMM12m
K-means from probabilistic perspective9m
K-means, M-step7m
Probabilistic PCA13m
EM for Probabilistic PCA7m
2 ejercicios de práctica
EM algorithm30m
Latent Variable Models and EM algorithm30m
2 horas para completar
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 completar
11 videos (Total 98 minutos)
11 videos
Mean field approximation13m
Example: Ising model15m
Variational EM & Review5m
Topic modeling5m
Dirichlet distribution6m
Latent Dirichlet Allocation5m
LDA: E-step, theta11m
LDA: E-step, z8m
LDA: M-step & prediction13m
Extensions of LDA5m
2 ejercicios de práctica
Variational inference15m
Latent Dirichlet Allocation15m
6 horas para completar
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 completar
11 videos (Total 122 minutos)
11 videos
Sampling from 1-d distributions13m
Markov Chains13m
Gibbs sampling12m
Example of Gibbs sampling7m
Metropolis-Hastings8m
Metropolis-Hastings: choosing the critic8m
Example of Metropolis-Hastings9m
Markov Chain Monte Carlo summary8m
MCMC for LDA15m
Bayesian Neural Networks11m
1 ejercicio de práctica
Markov Chain Monte Carlo30m
Reseñas
Principales reseñas sobre 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 ene. 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.
Acerca de Programa especializado: Aprendizaje automático avanzado
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.
Preguntas Frecuentes
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