Este curso forma parte de Programa especializado: modelos gráficos de probabilidades

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1,401 calificaciones

84 %

Daphne Koller

83.998 ya inscrito

ofrecido por

Programa especializado: modelos gráficos de probabilidades Universidad de Stanford

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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. 
This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.

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Curso 1 de 3 en

Programa especializado: modelos gráficos de probabilidades

Nivel avanzado

Aprox. 67 horas para completar

Inglés (English)

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

Programa especializado: modelos gráficos de probabilidades

Nivel avanzado

Aprox. 67 horas para completar

Inglés (English)

Instructor

Calificación del instructor

4,66/5 (83 calificaciones)

Daphne Koller

Professor

School of Engineering

87.688 alumnos

3 cursos

ofrecido por

Universidad de Stanford

Programa - Qué aprenderás en este curso

Calificación del contenido84%(3,988 calificaciones)

Semana

Semana 1

1 hora para completar

Introduction and Overview

1 hora para completar

4 videos (Total 35 minutos)

12 horas para completar

Bayesian Network (Directed Models)

12 horas para completar

15 videos (Total 190 minutos), 6 lecturas, 4 cuestionarios

15 videos

Semantics & Factorization17m

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

Semana

Semana 2

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

12 horas para completar

4 videos (Total 49 minutos)

Semana

Semana 3

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)

Semana

Semana 4

22 horas para completar

Decision Making

22 horas para completar

3 videos (Total 61 minutos)

Reseñas

4.6

311 reseña

5 stars
74,73 %
4 stars
17,77 %
3 stars
5,21 %
2 stars
0,99 %
1 star
1,28 %

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 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 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 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!!

Ver todas las reseñas

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.

Preguntas Frecuentes

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¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.
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

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Probabilistic Graphical Models 1: Representation

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Habilidades que obtendrás