Informações sobre o curso

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recebi um aumento ou promoção
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Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível avançado
Aprox. 64 horas para completar
Inglês
Legendas: Inglês

Habilidades que você terá

AlgorithmsExpectation–Maximization (EM) AlgorithmGraphical ModelMarkov Random Field

Resultados de carreira do aprendiz

43%

comecei uma nova carreira após concluir estes cursos

29%

consegui um benefício significativo de carreira com este curso

17%

recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível avançado
Aprox. 64 horas para completar
Inglês
Legendas: Inglês

oferecido por

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Universidade de Stanford

Programa - O que você aprenderá com este curso

Semana
1

Semana 1

16 minutos para concluir

Learning: Overview

16 minutos para concluir
1 vídeo (Total 16 mín.)
1 vídeos
1 hora para concluir

Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)

1 hora para concluir
6 vídeos (Total 59 mín.)
6 videos
Regularization: Cost Function 10min
Evaluating a Hypothesis 7min
Model Selection and Train Validation Test Sets 12min
Diagnosing Bias vs Variance 7min
Regularization and Bias Variance11min
2 horas para concluir

Parameter Estimation in Bayesian Networks

2 horas para concluir
5 vídeos (Total 77 mín.)
5 videos
Maximum Likelihood Estimation for Bayesian Networks15min
Bayesian Estimation15min
Bayesian Prediction13min
Bayesian Estimation for Bayesian Networks17min
2 exercícios práticos
Learning in Parametric Models18min
Bayesian Priors for BNs8min
Semana
2

Semana 2

21 horas para concluir

Learning Undirected Models

21 horas para concluir
3 vídeos (Total 52 mín.)
3 videos
Maximum Likelihood for Conditional Random Fields13min
MAP Estimation for MRFs and CRFs9min
1 exercício prático
Parameter Estimation in MNs6min
Semana
3

Semana 3

17 horas para concluir

Learning BN Structure

17 horas para concluir
7 vídeos (Total 106 mín.)
7 videos
Likelihood Scores16min
BIC and Asymptotic Consistency11min
Bayesian Scores20min
Learning Tree Structured Networks12min
Learning General Graphs: Heuristic Search23min
Learning General Graphs: Search and Decomposability15min
2 exercícios práticos
Structure Scores10min
Tree Learning and Hill Climbing8min
Semana
4

Semana 4

22 horas para concluir

Learning BNs with Incomplete Data

22 horas para concluir
5 vídeos (Total 83 mín.)
5 videos
Expectation Maximization - Intro16min
Analysis of EM Algorithm11min
EM in Practice11min
Latent Variables22min
2 exercícios práticos
Learning with Incomplete Data8min
Expectation Maximization14min

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Sobre Programa de cursos integrados Modelos Gráficos Probabilísticos

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems....
Modelos Gráficos Probabilísticos

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  • Compute the sufficient statistics of a data set that are necessary for learning a PGM from data

    Implement both maximum likelihood and Bayesian parameter estimation for Bayesian networks

    Implement maximum likelihood and MAP parameter estimation for Markov networks

    Formulate a structure learning problem as a combinatorial optimization task over a space of network structure, and evaluate which scoring function is appropriate for a given situation

    Utilize PGM inference algorithms in ways that support more effective parameter estimation for PGMs

    Implement the Expectation Maximization (EM) algorithm for Bayesian networks

    Honors track learners will get hands-on experience in implementing both EM and structure learning for tree-structured networks, and apply them to real-world tasks

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