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.
This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.
Este curso faz parte do Programa de cursos integrados Modelos Gráficos Probabilísticos
Probabilistic Graphical Models 1: Representation
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Informações sobre o curso
4.7
964 classificações
Habilidades que você terá
Bayesian NetworkGraphical ModelMarkov Random Field
Programa - O que você aprenderá com este curso
Semana
1Introduction 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.
4 vídeos (total de (Total 35 mín.) min), 1 teste
1 exercício prático
Basic Definitions8min
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.
15 vídeos (total de (Total 190 mín.) min), 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 Fundamentals6min
Bayesian Network Independencies10min
Octave/Matlab installation2min
Semana
2Template 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.
4 vídeos (total de (Total 66 mín.) min), 1 teste
4 videos
Temporal Models - DBNs23min
Temporal Models - HMMs12min
Plate Models20min
1 exercício prático
Template Models20min
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.
4 vídeos (total de (Total 49 mín.) min), 3 testes
4 videos
Tree-Structured CPDs14min
Independence of Causal Influence13min
Continuous Variables13min
2 exercícios práticos
Structured CPDs8min
BNs for Genetic Inheritance PA Quiz22min
Semana
3Markov 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.
7 vídeos (total de (Total 106 mín.) min), 3 testes
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 Networks8min
Independencies Revisited6min
Semana
4Decision 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.
3 vídeos (total de (Total 61 mín.) min), 3 testes
2 exercícios práticos
Decision Theory8min
Decision Making PA Quiz18min
25%
comecei uma nova carreira após concluir estes cursos
27%
consegui um benefício significativo de carreira com este curso
14%
recebi um aumento ou promoção
Melhores avaliações
por ST•Jul 13th 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 CM•Oct 23rd 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).
Instrutores
Daphne Koller
ProfessorSchool 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.
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Learning Outcomes: By the end of this course, you will be able to
Apply the basic process of representing a scenario as a Bayesian network or a Markov network
Analyze the independence properties implied by a PGM, and determine whether they are a good match for your distribution
Decide which family of PGMs is more appropriate for your task
Utilize extra structure in the local distribution for a Bayesian network to allow for a more compact representation, including tree-structured CPDs, logistic CPDs, and linear Gaussian CPDs
Represent a Markov network in terms of features, via a log-linear model
Encode temporal models as a Hidden Markov Model (HMM) or as a Dynamic Bayesian Network (DBN)
Encode domains with repeating structure via a plate model
Represent a decision making problem as an influence diagram, and be able to use that model to compute optimal decision strategies and information gathering strategies
Honors track learners will be able to apply these ideas for complex, real-world problems
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