Informações sobre o curso
4.6
171 classificações
28 avaliações
Programa de cursos integrados
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Nível avançado

Nível avançado

Horas para completar

Aprox. 24 horas para completar

Sugerido: 7 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês...

Habilidades que você terá

AlgorithmsExpectation–Maximization (EM) AlgorithmGraphical ModelMarkov Random Field
Programa de cursos integrados
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Nível avançado

Nível avançado

Horas para completar

Aprox. 24 horas para completar

Sugerido: 7 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês...

Programa - O que você aprenderá com este curso

Semana
1
Horas para completar
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....
Reading
1 vídeo (Total de 16 min)
Video1 vídeos
Horas para completar
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....
Reading
6 vídeos (Total de 59 min)
Video6 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
Horas para completar
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....
Reading
5 vídeos (Total de 77 min), 2 testes
Video5 videos
Maximum Likelihood Estimation for Bayesian Networks15min
Bayesian Estimation15min
Bayesian Prediction13min
Bayesian Estimation for Bayesian Networks17min
Quiz2 exercícios práticos
Learning in Parametric Models18min
Bayesian Priors for BNs8min
Semana
2
Horas para completar
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....
Reading
3 vídeos (Total de 52 min), 2 testes
Video3 videos
Maximum Likelihood for Conditional Random Fields13min
MAP Estimation for MRFs and CRFs9min
Quiz1 exercício prático
Parameter Estimation in MNs6min
Semana
3
Horas para completar
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....
Reading
7 vídeos (Total de 106 min), 3 testes
Video7 videos
Likelihood Scores16min
BIC and Asymptotic Consistency11min
Bayesian Scores20min
Learning Tree Structured Networks12min
Learning General Graphs: Heuristic Search23min
Learning General Graphs: Search and Decomposability15min
Quiz2 exercícios práticos
Structure Scores10min
Tree Learning and Hill Climbing8min
Semana
4
Horas para completar
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....
Reading
5 vídeos (Total de 83 min), 3 testes
Video5 videos
Expectation Maximization - Intro16min
Analysis of EM Algorithm11min
EM in Practice11min
Latent Variables22min
Quiz2 exercícios práticos
Learning with Incomplete Data8min
Expectation Maximization14min
4.6
28 avaliaçõesChevron Right
Benefício de carreira

83%

consegui um benefício significativo de carreira com este curso
Promoção de carreira

14%

recebi um aumento ou promoção

Melhores avaliações

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.

Instrutores

Avatar

Daphne Koller

Professor
School of Engineering

Sobre Stanford University

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 Probabilistic Graphical Models

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....
Probabilistic Graphical Models

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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