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 forma parte de Programa Especializado - Probabilistic Graphical Models
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Programa Especializado - Probabilistic Graphical Models Universidad de StanfordAcerca de este Curso
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Nivel avanzado
Aprox. 67 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish)
Habilidades que obtendrás
Bayesian NetworkGraphical ModelMarkov Random Field
Resultados profesionales del estudiante
20%
comenzó una nueva carrera después de completar estos cursos
18%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 3 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel avanzado
Aprox. 67 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish)
ofrecido por
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.
Programa - Qué aprenderás en este curso
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.
1 hora para completar
4 videos (Total 35 minutos)
1 ejercicio de práctica
Basic Definitions30m
12 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.
12 horas para completar
15 videos (Total 190 minutos), 6 lecturas, 4 cuestionarios
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 Fundamentals30m
Bayesian Network Independencies30m
Octave/Matlab installation30m
2 horas 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.
2 horas para completar
4 videos (Total 66 minutos)
4 videos
Temporal Models - DBNs23m
Temporal Models - HMMs12m
Plate Models20m
1 ejercicio de práctica
Template Models30m
12 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.
12 horas para completar
4 videos (Total 49 minutos)
4 videos
Tree-Structured CPDs14m
Independence of Causal Influence13m
Continuous Variables13m
2 ejercicios de práctica
Structured CPDs30m
BNs for Genetic Inheritance PA Quiz30m
18 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.
18 horas para completar
7 videos (Total 106 minutos)
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 Networks30m
Independencies Revisited30m
22 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.
22 horas para completar
3 videos (Total 61 minutos)
2 ejercicios de práctica
Decision Theory30m
Decision Making PA Quiz30m
Reseñas
Principales reseñas sobre 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 oct. 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).
Acerca de 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
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