Este curso faz parte do Programa de cursos integrados Advanced Machine Learning

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Programa de cursos integrados Advanced Machine Learning

National Research University Higher School of Economics

Informações sobre o curso

4.5

210 classificações

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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....

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

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

Legendas: English...

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

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

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

Legendas: English...

Week

1Welcome 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 vídeos (Total de 55 min), 1 leitura, 2 testes

Bayesian approach to statistics5min

How to define a model3min

Example: thief & alarm11min

Linear regression10min

Analytical inference3min

Conjugate distributions2min

Example: Normal, precision5min

Example: Bernoulli4min

MLE estimation of Gaussian mean10min

Introduction to Bayesian methods20min

Conjugate priors12min

Week

2This 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 vídeos (Total de 168 min), 3 testes

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

EM algorithm8min

Latent Variable Models and EM algorithm10min

Week

3This 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 vídeos (Total de 98 min), 2 testes

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

Variational inference15min

Latent Dirichlet Allocation15min

Week

4This 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 vídeos (Total de 122 min), 2 testes

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

Markov Chain Monte Carlo20min

4.5

consegui um benefício significativo de carreira com este curso

por JG•Nov 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 AE•May 9th 2018

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

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...

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....

When will I have access to the lectures and assignments?

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.

What will I get if I subscribe to this Specialization?

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.

What is the refund policy?

Is financial aid available?

What background knowledge is necessary?

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

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