Acerca de este Curso
4.7
942 calificaciones
217 revisiones
100 % en línea

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Nivel avanzado

Nivel avanzado

Horas para completar

Aprox. 29 horas para completar

Sugerido: 7 hours/week...
Idiomas disponibles

Inglés (English)

Subtítulos: Inglés (English)

Habilidades que obtendrás

Bayesian NetworkGraphical ModelMarkov Random Field
100 % en línea

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Nivel avanzado

Nivel avanzado

Horas para completar

Aprox. 29 horas para completar

Sugerido: 7 hours/week...
Idiomas disponibles

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
Horas para completar
1 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....
Reading
4 videos (Total 35 minutos), 1 quiz
Video4 videos
Overview and Motivation19m
Distributions4m
Factors6m
Quiz1 ejercicio de práctica
Basic Definitions8m
Horas para completar
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....
Reading
15 videos (Total 190 minutos), 6 readings, 4 quizzes
Video15 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
Reading6 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
Quiz3 ejercicios de práctica
Bayesian Network Fundamentals6m
Bayesian Network Independencies10m
Octave/Matlab installation2m
Semana
2
Horas para completar
1 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....
Reading
4 videos (Total 66 minutos), 1 quiz
Video4 videos
Temporal Models - DBNs23m
Temporal Models - HMMs12m
Plate Models20m
Quiz1 ejercicio de práctica
Template Models20m
Horas para completar
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....
Reading
4 videos (Total 49 minutos), 3 quizzes
Video4 videos
Tree-Structured CPDs14m
Independence of Causal Influence13m
Continuous Variables13m
Quiz2 ejercicios de práctica
Structured CPDs8m
BNs for Genetic Inheritance PA Quiz22m
Semana
3
Horas para completar
17 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....
Reading
7 videos (Total 106 minutos), 3 quizzes
Video7 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
Quiz2 ejercicios de práctica
Markov Networks8m
Independencies Revisited6m
Semana
4
Horas para completar
21 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....
Reading
3 videos (Total 61 minutos), 3 quizzes
Video3 videos
Utility Functions18m
Value of Perfect Information17m
Quiz2 ejercicios de práctica
Decision Theory8m
Decision Making PA Quiz18m
4.7
217 revisionesChevron Right
Dirección de la carrera

25%

comenzó una nueva carrera después de completar estos cursos
Beneficio de la carrera

27%

consiguió un beneficio tangible en su carrera profesional gracias a este curso
Promoción de la carrera

14%

consiguió un aumento de sueldo o ascenso

Principales revisiones

por STJul 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 CMOct 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).

Instructor

Avatar

Daphne Koller

Professor
School 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....
Probabilistic Graphical Models

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

  • 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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