Об этом курсе
4.5
Оценки: 210
Рецензии: 66
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

Продвинутый уровень

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Предполагаемая нагрузка: 6 weeks of study, 6 hours/week

Прибл. 38 ч. на завершение
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English

Субтитры: English

Приобретаемые навыки

Bayesian OptimizationGaussian ProcessMarkov Chain Monte Carlo (MCMC)Variational Bayesian Methods
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Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

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Advanced Level

Продвинутый уровень

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Предполагаемая нагрузка: 6 weeks of study, 6 hours/week

Прибл. 38 ч. на завершение
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English

Субтитры: English

Программа курса: что вы изучите

1

Раздел
Clock
2 ч. на завершение

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 видео (всего 55 мин.), 1 материал для самостоятельного изучения, 2 тестов
Video9 видео
Bayesian approach to statistics5мин
How to define a model3мин
Example: thief & alarm11мин
Linear regression10мин
Analytical inference3мин
Conjugate distributions2мин
Example: Normal, precision5мин
Example: Bernoulli4мин
Reading1 материал для самостоятельного изучения
MLE estimation of Gaussian mean10мин
Quiz2 практического упражнения
Introduction to Bayesian methods20мин
Conjugate priors12мин

2

Раздел
Clock
7 ч. на завершение

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 видео (всего 168 мин.), 3 тестов
Video17 видео
Probabilistic clustering6мин
Gaussian Mixture Model10мин
Training GMM10мин
Example of GMM training10мин
Jensen's inequality & Kullback Leibler divergence9мин
Expectation-Maximization algorithm10мин
E-step details12мин
M-step details6мин
Example: EM for discrete mixture, E-step10мин
Example: EM for discrete mixture, M-step12мин
Summary of Expectation Maximization6мин
General EM for GMM12мин
K-means from probabilistic perspective9мин
K-means, M-step7мин
Probabilistic PCA13мин
EM for Probabilistic PCA7мин
Quiz2 практического упражнения
EM algorithm8мин
Latent Variable Models and EM algorithm10мин

3

Раздел
Clock
2 ч. на завершение

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 видео (всего 98 мин.), 2 тестов
Video11 видео
Mean field approximation13мин
Example: Ising model15мин
Variational EM & Review5мин
Topic modeling5мин
Dirichlet distribution6мин
Latent Dirichlet Allocation5мин
LDA: E-step, theta11мин
LDA: E-step, z8мин
LDA: M-step & prediction13мин
Extensions of LDA5мин
Quiz2 практического упражнения
Variational inference15мин
Latent Dirichlet Allocation15мин

4

Раздел
Clock
6 ч. на завершение

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 видео (всего 122 мин.), 2 тестов
Video11 видео
Sampling from 1-d distributions13мин
Markov Chains13мин
Gibbs sampling12мин
Example of Gibbs sampling7мин
Metropolis-Hastings8мин
Metropolis-Hastings: choosing the critic8мин
Example of Metropolis-Hastings9мин
Markov Chain Monte Carlo summary8мин
MCMC for LDA15мин
Bayesian Neural Networks11мин
Quiz1 практическое упражнение
Markov Chain Monte Carlo20мин
4.5
Briefcase

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получил значимые преимущества в карьере благодаря этому курсу

Лучшие рецензии

автор: 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.

автор: AEMay 9th 2018

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

Преподавателя

Daniil Polykovskiy

Researcher
HSE Faculty of Computer Science

Alexander Novikov

Researcher
HSE Faculty of Computer Science

О 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...

О специализации ''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

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

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