Informações sobre o curso
4.6
305 classificações
49 avaliações
Programa de cursos integrados
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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. 23 horas para completar

Sugerido: 7 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês

Habilidades que você terá

InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation
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. 23 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
25 minutos para concluir

Inference Overview

This module provides a high-level overview of the main types of inference tasks typically encountered in graphical models: conditional probability queries, and finding the most likely assignment (MAP inference)....
Reading
2 vídeos (total de (Total 25 mín.) min)
Video2 videos
Overview: MAP Inference9min
Horas para completar
1 hora para concluir

Variable Elimination

This module presents the simplest algorithm for exact inference in graphical models: variable elimination. We describe the algorithm, and analyze its complexity in terms of properties of the graph structure....
Reading
4 vídeos (total de (Total 56 mín.) min), 1 teste
Video4 videos
Complexity of Variable Elimination12min
Graph-Based Perspective on Variable Elimination15min
Finding Elimination Orderings11min
Quiz1 exercício prático
Variable Elimination18min
Semana
2
Horas para completar
18 horas para concluir

Belief Propagation Algorithms

This module describes an alternative view of exact inference in graphical models: that of message passing between clusters each of which encodes a factor over a subset of variables. This framework provides a basis for a variety of exact and approximate inference algorithms. We focus here on the basic framework and on its instantiation in the exact case of clique tree propagation. An optional lesson describes the loopy belief propagation (LBP) algorithm and its properties....
Reading
9 vídeos (total de (Total 150 mín.) min), 3 testes
Video9 videos
Properties of Cluster Graphs15min
Properties of Belief Propagation9min
Clique Tree Algorithm - Correctness18min
Clique Tree Algorithm - Computation16min
Clique Trees and Independence15min
Clique Trees and VE16min
BP In Practice15min
Loopy BP and Message Decoding21min
Quiz2 exercícios práticos
Message Passing in Cluster Graphs10min
Clique Tree Algorithm10min
Semana
3
Horas para completar
1 hora para concluir

MAP Algorithms

This module describes algorithms for finding the most likely assignment for a distribution encoded as a PGM (a task known as MAP inference). We describe message passing algorithms, which are very similar to the algorithms for computing conditional probabilities, except that we need to also consider how to decode the results to construct a single assignment. In an optional module, we describe a few other algorithms that are able to use very different techniques by exploiting the combinatorial optimization nature of the MAP task....
Reading
5 vídeos (total de (Total 74 mín.) min), 1 teste
Video5 videos
Finding a MAP Assignment3min
Tractable MAP Problems15min
Dual Decomposition - Intuition17min
Dual Decomposition - Algorithm16min
Quiz1 exercício prático
MAP Message Passing4min
Semana
4
Horas para completar
14 horas para concluir

Sampling Methods

In this module, we discuss a class of algorithms that uses random sampling to provide approximate answers to conditional probability queries. Most commonly used among these is the class of Markov Chain Monte Carlo (MCMC) algorithms, which includes the simple Gibbs sampling algorithm, as well as a family of methods known as Metropolis-Hastings....
Reading
5 vídeos (total de (Total 100 mín.) min), 3 testes
Video5 videos
Markov Chain Monte Carlo14min
Using a Markov Chain15min
Gibbs Sampling19min
Metropolis Hastings Algorithm27min
Quiz2 exercícios práticos
Sampling Methods14min
Sampling Methods PA Quiz8min
Horas para completar
26 minutos para concluir

Inference in Temporal Models

In this brief lesson, we discuss some of the complexities of applying some of the exact or approximate inference algorithms that we learned earlier in this course to dynamic Bayesian networks....
Reading
1 vídeo (total de (Total 20 mín.) min), 1 teste
Video1 vídeos
Quiz1 exercício prático
Inference in Temporal Models6min
4.6
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Melhores avaliações

por LLMar 12th 2017

Thanks a lot for professor D.K.'s great course for PGM inference part. Really a very good starting point for PGM model and preparation for learning part.

por YPMay 29th 2017

I learned pretty much from this course. It answered my quandaries from the representation course, and as well deepened my understanding of PGM.

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.

  • Execute the basic steps of a variable elimination or message passing algorithm

    Understand how properties of the graph structure influence the complexity of exact inference, and thereby estimate whether exact inference is likely to be feasible

    Go through the basic steps of an MCMC algorithm, both Gibbs sampling and Metropolis Hastings

    Understand how properties of the PGM influence the efficacy of sampling methods, and thereby estimate whether MCMC algorithms are likely to be effective

    Design Metropolis Hastings proposal distributions that are more likely to give good results

    Compute a MAP assignment by exact inference

    Honors track learners will be able to implement message passing algorithms and MCMC algorithms, and apply them to a real world problem

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