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
Probabilistic Graphical Models 1: Representation
ofrecido por
Acerca de este Curso
72,678 vistas recientes
Resultados profesionales del estudiante
23%
comenzó una nueva carrera después de completar estos cursos
22%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
11%
consiguió un aumento de sueldo o ascenso
Habilidades que obtendrás
Bayesian NetworkGraphical ModelMarkov Random Field
Resultados profesionales del estudiante
23%
comenzó una nueva carrera después de completar estos cursos
22%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
11%
consiguió un aumento de sueldo o ascenso
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. 30 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
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 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.
10 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 Fundamentals6m
Bayesian Network Independencies10m
Octave/Matlab installation2m
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.
1 hora para completar
4 videos (Total 66 minutos)
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.
11 horas para completar
4 videos (Total 49 minutos)
4 videos
Tree-Structured CPDs14m
Independence of Causal Influence13m
Continuous Variables13m
2 ejercicios de práctica
Structured CPDs8m
BNs for Genetic Inheritance PA Quiz22m
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.
17 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 Networks8m
Independencies Revisited6m
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.
21 horas para completar
3 videos (Total 61 minutos)
2 ejercicios de práctica
Decision Theory8m
Decision Making PA Quiz18m
Revisiones
Principales revisiones sobre PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
por AMNov 2, 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 BMJun 27, 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 STJul 12, 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 22, 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).
por MBAug 30, 2018
Excellent course, the effort of the instructor is well reflected in the content and the exercices. A must for every serious student on (decision theory or markov random fields tasks.
por AKNov 12, 2016
Superb exposition. Makes me want to continue learning till the very end of this course. Very intuitive explanations. Plan to complete all courses offered in this specialization.
por CPJan 15, 2020
Simply excellent. A wonderful course to begin the representation of PGM. Be advised.... this can get quite advanced. It's all about that Bayes, 'bout that Bayes.... no trouble.
por DPJun 17, 2017
I have Actually Earned Three Years of my life (at least) and one possible patent because of this course.\n\nThank You Daphne Mam. God Bless Everybody Associated with it.
por CBJul 16, 2017
learned a lot. lectures were easy to follow and the textbook was able to more fully explain things when I needed it. looking forward to the next course in the series.
por ALJul 19, 2019
Some parts are challenging enough in the PAs, if you are familiar with Matlab this course is a great opportunity to get familiar with PGMs and learn to handle these.
por CCMar 24, 2020
really great course! very clear and logical structure. I completed a graphical models course as part of my master's degree, and this really helped to consolidate it
por PSDec 7, 2016
Very well designed. There were areas here I struggled with the technical details and had to read up a lot to understand. The assignments are very well designed.
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.
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.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
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.
¿Qué recibiré si me suscribo a este Programa especializado?
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.
¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
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
¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.
