About this Course
최근 조회 75,729

다음 전문 분야의 1개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

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

영어

자막: 영어, 한국어

귀하가 습득할 기술

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

다음 전문 분야의 1개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

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

영어

자막: 영어, 한국어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 2시간 필요

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.

...
9 videos (Total 55 min), 1 reading, 2 quizzes
9개의 동영상
Example: thief & alarm11m
Linear regression10m
Analytical inference3m
Conjugate distributions2m
Example: Normal, precision5m
Example: Bernoulli4m
1개의 읽기 자료
MLE estimation of Gaussian mean10m
2개 연습문제
Introduction to Bayesian methods20m
Conjugate priors12m
2
완료하는 데 6시간 필요

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.

...
17 videos (Total 168 min), 3 quizzes
17개의 동영상
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
2개 연습문제
EM algorithm8m
Latent Variable Models and EM algorithm10m
3
완료하는 데 2시간 필요

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

...
11 videos (Total 98 min), 2 quizzes
11개의 동영상
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
2개 연습문제
Variational inference15m
Latent Dirichlet Allocation15m
4
완료하는 데 5시간 필요

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.

...
11 videos (Total 122 min), 2 quizzes
11개의 동영상
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
1개 연습문제
Markov Chain Monte Carlo20m
4.6
98개의 리뷰Chevron Right

60%

이 강좌를 수료한 후 새로운 경력 시작하기

36%

이 강좌를 통해 확실한 경력상 이점 얻기

Bayesian Methods for Machine Learning의 최상위 리뷰

대학: 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.

대학: LBJun 7th 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.

강사

Avatar

Daniil Polykovskiy

Researcher
HSE Faculty of Computer Science
Avatar

Alexander Novikov

Researcher
HSE Faculty of Computer Science

국립 연구 고등 경제 대학 정보

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. Learn more on www.hse.ru...

고급 기계 학습 전문 분야 정보

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....
고급 기계 학습

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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

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