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.
Este curso faz parte do Programa de cursos integrados Modelos Gráficos Probabilísticos
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Programa de cursos integrados Modelos Gráficos Probabilísticos Universidade de StanfordInformações sobre o curso
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Nível avançado
Aprox. 67 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
Habilidades que você terá
Bayesian NetworkGraphical ModelMarkov Random Field
Resultados de carreira do aprendiz
20%
comecei uma nova carreira após concluir estes cursos
18%
consegui um benefício significativo de carreira com este curso
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. 67 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
oferecido por
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.
Programa - O que você aprenderá com este curso
1 hora para concluir
Introduction and Overview
This module provides an overall introduction to probabilistic graphical models, and defines a few of the key concepts that will be used later in the course.
1 hora para concluir
4 vídeos (Total 35 mín.)
1 exercício prático
Basic Definitions30min
12 horas para concluir
Bayesian Network (Directed Models)
In this module, we define the Bayesian network representation and its semantics. We also analyze the relationship between the graph structure and the independence properties of a distribution represented over that graph. Finally, we give some practical tips on how to model a real-world situation as a Bayesian network.
12 horas para concluir
15 vídeos (Total 190 mín.), 6 leituras, 4 testes
15 videos
Reasoning Patterns9min
Flow of Probabilistic Influence14min
Conditional Independence12min
Independencies in Bayesian Networks18min
Naive Bayes9min
Application - Medical Diagnosis9min
Knowledge Engineering Example - SAMIAM14min
Basic Operations 13min
Moving Data Around 16min
Computing On Data 13min
Plotting Data 9min
Control Statements: for, while, if statements 12min
Vectorization 13min
Working on and Submitting Programming Exercises 3min
6 leituras
Setting Up Your Programming Assignment Environment10min
Installing Octave/MATLAB on Windows10min
Installing Octave/MATLAB on Mac OS X (10.10 Yosemite and 10.9 Mavericks)10min
Installing Octave/MATLAB on Mac OS X (10.8 Mountain Lion and Earlier)10min
Installing Octave/MATLAB on GNU/Linux10min
More Octave/MATLAB resources10min
3 exercícios práticos
Bayesian Network Fundamentals30min
Bayesian Network Independencies30min
Octave/Matlab installation30min
2 horas para concluir
Template Models for Bayesian Networks
In many cases, we need to model distributions that have a recurring structure. In this module, we describe representations for two such situations. One is temporal scenarios, where we want to model a probabilistic structure that holds constant over time; here, we use Hidden Markov Models, or, more generally, Dynamic Bayesian Networks. The other is aimed at scenarios that involve multiple similar entities, each of whose properties is governed by a similar model; here, we use Plate Models.
2 horas para concluir
4 vídeos (Total 66 mín.)
4 videos
Temporal Models - DBNs23min
Temporal Models - HMMs12min
Plate Models20min
1 exercício prático
Template Models30min
12 horas para concluir
Structured CPDs for Bayesian Networks
A table-based representation of a CPD in a Bayesian network has a size that grows exponentially in the number of parents. There are a variety of other form of CPD that exploit some type of structure in the dependency model to allow for a much more compact representation. Here we describe a number of the ones most commonly used in practice.
12 horas para concluir
4 vídeos (Total 49 mín.)
4 videos
Tree-Structured CPDs14min
Independence of Causal Influence13min
Continuous Variables13min
2 exercícios práticos
Structured CPDs30min
BNs for Genetic Inheritance PA Quiz30min
18 horas para concluir
Markov Networks (Undirected Models)
In this module, we describe Markov networks (also called Markov random fields): probabilistic graphical models based on an undirected graph representation. We discuss the representation of these models and their semantics. We also analyze the independence properties of distributions encoded by these graphs, and their relationship to the graph structure. We compare these independencies to those encoded by a Bayesian network, giving us some insight on which type of model is more suitable for which scenarios.
18 horas para concluir
7 vídeos (Total 106 mín.)
7 videos
General Gibbs Distribution15min
Conditional Random Fields22min
Independencies in Markov Networks4min
I-maps and perfect maps20min
Log-Linear Models22min
Shared Features in Log-Linear Models8min
2 exercícios práticos
Markov Networks30min
Independencies Revisited30min
22 horas para concluir
Decision Making
In this module, we discuss the task of decision making under uncertainty. We describe the framework of decision theory, including some aspects of utility functions. We then talk about how decision making scenarios can be encoded as a graphical model called an Influence Diagram, and how such models provide insight both into decision making and the value of information gathering.
22 horas para concluir
3 vídeos (Total 61 mín.)
2 exercícios práticos
Decision Theory30min
Decision Making PA Quiz30min
Avaliações
Principais avaliações do PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
por AM2 de Nov de 2018
Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.
por BM27 de Jun de 2017
The lecture was a bit too compact and unsystematic. However, if you also do a lot of reading of the textbook, you can learn a lot. Besides, the Quiz and Programming task are of high qualities.
por ST12 de Jul de 2017
Prof. Koller did a great job communicating difficult material in an accessible manner. Thanks to her for starting Coursera and offering this advanced course so that we can all learn...Kudos!!
por CM22 de Out de 2017
The course was deep, and well-taught. This is not a spoon-feeding course like some others. The only downside were some "mechanical" problems (e.g. code submission didn't work for me).
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.
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