Informações sobre o curso
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Nível avançado

Aprox. 24 horas para completar


Legendas: Inglês

Habilidades que você terá

AlgorithmsExpectation–Maximization (EM) AlgorithmGraphical ModelMarkov Random Field

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível avançado

Aprox. 24 horas para completar


Legendas: Inglês

Programa - O que você aprenderá com este curso

16 minutos para concluir

Learning: Overview

This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.

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)

This module contains some basic concepts from the general framework of machine learning, taken from Professor Andrew Ng's Stanford class offered on Coursera. Many of these concepts are highly relevant to the problems we'll tackle in this course.

6 vídeos ((Total 59 mín.))
6 videos
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

This module discusses the simples and most basic of the learning problems in probabilistic graphical models: that of parameter estimation in a Bayesian network. We discuss maximum likelihood estimation, and the issues with it. We then discuss Bayesian estimation and how it can ameliorate these problems.

5 vídeos ((Total 77 mín.)), 2 testes
5 videos
Bayesian Prediction13min
Bayesian Estimation for Bayesian Networks17min
2 exercícios práticos
Learning in Parametric Models18min
Bayesian Priors for BNs8min
21 horas para concluir

Learning Undirected Models

In this module, we discuss the parameter estimation problem for Markov networks - undirected graphical models. This task is considerably more complex, both conceptually and computationally, than parameter estimation for Bayesian networks, due to the issues presented by the global partition function.

3 vídeos ((Total 52 mín.)), 2 testes
1 exercício prático
Parameter Estimation in MNs6min
17 horas para concluir

Learning BN Structure

This module discusses the problem of learning the structure of Bayesian networks. We first discuss how this problem can be formulated as an optimization problem over a space of graph structures, and what are good ways to score different structures so as to trade off fit to data and model complexity. We then talk about how the optimization problem can be solved: exactly in a few cases, approximately in most others.

7 vídeos ((Total 106 mín.)), 3 testes
7 videos
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
22 horas para concluir

Learning BNs with Incomplete Data

In this module, we discuss the problem of learning models in cases where some of the variables in some of the data cases are not fully observed. We discuss why this situation is considerably more complex than the fully observable case. We then present the Expectation Maximization (EM) algorithm, which is used in a wide variety of problems.

5 vídeos ((Total 83 mín.)), 3 testes
5 videos
EM in Practice11min
Latent Variables22min
2 exercícios práticos
Learning with Incomplete Data8min
Expectation Maximization14min
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Principais avaliações do Probabilistic Graphical Models 3: Learning

por LLJan 30th 2018

very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.

por ZZFeb 14th 2017

Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.



Daphne Koller

School of Engineering

Sobre Universidade de Stanford

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States....

Sobre o 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

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

  • 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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