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: modelos gráficos de probabilidades
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Programa especializado: modelos gráficos de probabilidades Universidad de StanfordAcerca de este Curso
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Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
Habilidades que obtendrás
- Bayesian Network
- Graphical Model
- Markov Random Field
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
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
Nivel avanzado
Aprox. 67 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), 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)
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
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)
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)
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)
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)
Reseñas
- 5 stars74,85 %
- 4 stars17,68 %
- 3 stars5,21 %
- 2 stars0,94 %
- 1 star1,30 %
Principales reseñas sobre PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
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 SS17 de oct. de 2016
Thanks to this course, Probabilistic Graphical Models are not anymore an esoteric subject! I am really looking for the second part of the course.
por NB9 de sep. de 2020
Great content and easy to pick up. Only issue was with downloaded Octave software. Does not work, despite multiple downloads on different machines
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.
Acerca de Programa especializado: modelos gráficos de probabilidades
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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