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 faz parte do Programa de cursos integrados Modelos Gráficos Probabilísticos
81.884 já se inscreveram
oferecido por
Programa de cursos integrados Modelos Gráficos Probabilísticos Universidade de StanfordInformações sobre o curso
31.927 visualizações recentes
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível avançado
Aprox. 67 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Inglês, Espanhol
Habilidades que você terá
- Bayesian Network
- Graphical Model
- Markov Random Field
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível avançado
Aprox. 67 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Inglês, Espanhol
oferecido por
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.
Programa - O que você aprenderá com este curso
1 hora para concluir
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 concluir
4 vídeos (Total 35 mín.)
12 horas para concluir
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 concluir
15 vídeos (Total 190 mín.), 6 leituras, 4 testes
2 horas para concluir
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 concluir
4 vídeos (Total 66 mín.)
12 horas para concluir
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 concluir
4 vídeos (Total 49 mín.)
18 horas para concluir
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 concluir
7 vídeos (Total 106 mín.)
22 horas para concluir
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 concluir
3 vídeos (Total 61 mín.)
Avaliações
- 5 stars74,83%
- 4 stars17,71%
- 3 stars5,20%
- 2 stars0,93%
- 1 star1,30%
Principais avaliações do PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
por JP15 de jun de 2022
A comprehensive introduction and review of how to represent joint probability distributions as graphs and basic causal reasoning and decision making.
por SR1 de mar de 2018
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
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 RS19 de mar de 2018
A good introduction to PGM, from very basic concepts to some move in-depth features. A big disadvantage is Matlab/Octave programming assignments.
Sobre 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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
O que recebo ao me inscrever nesta Especialização?
Existe algum auxílio financeiro disponível?
Learning Outcomes: By the end of this course, you will be able to
Mais dúvidas? Visite o Central de Ajuda ao estudante.
