Informações sobre o curso
4.5
210 classificações
66 avaliações
Bayesian methods are used in lots of fields: 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 really desirable feature for fields like medicine. When Bayesian methods are applied to deep learning, it turns out that they allow you to compress your models 100 folds, and automatically tune hyperparametrs, saving your time and money. In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can fully automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases be found with Bayesian methods....
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Advanced Level

Nível avançado

Clock

Approx. 37 hours to complete

Sugerido: 6 weeks of study, 6 hours/week...
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English

Legendas: English...

Habilidades que você terá

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

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Calendar

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Advanced Level

Nível avançado

Clock

Approx. 37 hours to complete

Sugerido: 6 weeks of study, 6 hours/week...
Comment Dots

English

Legendas: English...

Programa - O que você aprenderá com este curso

Week
1
Clock
2 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....
Reading
9 vídeos (Total de 55 min), 1 leitura, 2 testes
Video9 videos
Bayesian approach to statistics5min
How to define a model3min
Example: thief & alarm11min
Linear regression10min
Analytical inference3min
Conjugate distributions2min
Example: Normal, precision5min
Example: Bernoulli4min
Reading1 leituras
MLE estimation of Gaussian mean10min
Quiz2 exercícios práticos
Introduction to Bayesian methods20min
Conjugate priors12min
Week
2
Clock
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....
Reading
17 vídeos (Total de 168 min), 3 testes
Video17 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
Quiz2 exercícios práticos
EM algorithm8min
Latent Variable Models and EM algorithm10min
Week
3
Clock
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...
Reading
11 vídeos (Total de 98 min), 2 testes
Video11 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
Quiz2 exercícios práticos
Variational inference15min
Latent Dirichlet Allocation15min
Week
4
Clock
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....
Reading
11 vídeos (Total de 122 min), 2 testes
Video11 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
Quiz1 exercício prático
Markov Chain Monte Carlo20min
4.5
Briefcase

83%

consegui um benefício significativo de carreira com este curso

Melhores avaliações

por 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.

por AEMay 9th 2018

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

Instrutores

Daniil Polykovskiy

Researcher
HSE Faculty of Computer Science

Alexander Novikov

Researcher
HSE Faculty of Computer Science

Sobre 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...

Sobre o Programa de cursos integrados Advanced Machine Learning

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

Perguntas Frequentes – FAQ

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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

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