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 forma parte de Programa Especializado - Probabilistic Graphical Models
Probabilistic Graphical Models 1: Representation
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66,036Habilidades que obtendrás
Bayesian NetworkGraphical ModelMarkov Random Field
Programa - Qué aprenderás en este curso
Semana
11 hora para completar
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.
4 videos (Total 35 minutos), 1 quiz
1 ejercicio de práctica
Basic Definitions8m
10 horas para completar
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 videos (Total 190 minutos), 6 readings, 4 quizzes
15 videos
Reasoning Patterns9m
Flow of Probabilistic Influence14m
Conditional Independence12m
Independencies in Bayesian Networks18m
Naive Bayes9m
Application - Medical Diagnosis9m
Knowledge Engineering Example - SAMIAM14m
Basic Operations 13m
Moving Data Around 16m
Computing On Data 13m
Plotting Data 9m
Control Statements: for, while, if statements 12m
Vectorization 13m
Working on and Submitting Programming Exercises 3m
6 lecturas
Setting Up Your Programming Assignment Environment10m
Installing Octave/MATLAB on Windows10m
Installing Octave/MATLAB on Mac OS X (10.10 Yosemite and 10.9 Mavericks)10m
Installing Octave/MATLAB on Mac OS X (10.8 Mountain Lion and Earlier)10m
Installing Octave/MATLAB on GNU/Linux10m
More Octave/MATLAB resources10m
3 ejercicios de práctica
Bayesian Network Fundamentals6m
Bayesian Network Independencies10m
Octave/Matlab installation2m
Semana
21 hora para completar
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.
4 videos (Total 66 minutos), 1 quiz
4 videos
Temporal Models - DBNs23m
Temporal Models - HMMs12m
Plate Models20m
1 ejercicio de práctica
Template Models20m
11 horas para completar
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 videos (Total 49 minutos), 3 quizzes
4 videos
Tree-Structured CPDs14m
Independence of Causal Influence13m
Continuous Variables13m
2 ejercicios de práctica
Structured CPDs8m
BNs for Genetic Inheritance PA Quiz22m
Semana
317 horas para completar
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.
7 videos (Total 106 minutos), 3 quizzes
7 videos
General Gibbs Distribution15m
Conditional Random Fields22m
Independencies in Markov Networks4m
I-maps and perfect maps20m
Log-Linear Models22m
Shared Features in Log-Linear Models8m
2 ejercicios de práctica
Markov Networks8m
Independencies Revisited6m
Semana
421 horas para completar
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.
3 videos (Total 61 minutos), 3 quizzes
2 ejercicios de práctica
Decision Theory8m
Decision Making PA Quiz18m
Semana
51 hora para completar
Knowledge Engineering & Summary
This module provides an overview of graphical model representations and some of the real-world considerations when modeling a scenario as a graphical model. It also includes the course final exam.
1 video (Total 23 minutos), 1 quiz
1 video
1 ejercicio de práctica
Representation Final Exam16m
25%
comenzó una nueva carrera después de completar estos cursos
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consiguió un beneficio tangible en su carrera profesional gracias a este curso
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consiguió un aumento de sueldo o ascenso
Principales revisiones
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!!
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).
Instructor
Daphne Koller
ProfessorSchool of Engineering
Acerca de Universidad 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.
Acerca del programa especializado 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.
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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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