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

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1,405 calificaciones

84 %

Daphne Koller

85.326 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. 66 horas para completar

Inglés (English)

Habilidades que obtendrás

Bayesian Network
Graphical Model
Markov Random Field

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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. 66 horas para completar

Inglés (English)

Instructor

Calificación del instructor

4,67/5 (84 calificaciones)

Daphne Koller

Professor

School of Engineering

89.086 alumnos

3 cursos

Ofrecido por

Universidad de Stanford

Programa - Qué aprenderás en este curso

Calificación del contenido84%(4,003 calificaciones)

Semana1

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 & Factorization17mReasoning Patterns9mFlow of Probabilistic Influence14mConditional Independence12mIndependencies in Bayesian Networks18mNaive Bayes9mApplication - Medical Diagnosis9mKnowledge Engineering Example - SAMIAM14mBasic Operations 13mMoving Data Around 16mComputing On Data 13mPlotting Data 9mControl Statements: for, while, if statements 12mVectorization 13mWorking on and Submitting Programming Exercises 3m

6 lecturas

Setting Up Your Programming Assignment Environment10mInstalling Octave/MATLAB on Windows10mInstalling Octave/MATLAB on Mac OS X (10.10 Yosemite and 10.9 Mavericks)10mInstalling Octave/MATLAB on Mac OS X (10.8 Mountain Lion and Earlier)10mInstalling Octave/MATLAB on GNU/Linux10mMore Octave/MATLAB resources10m

3 ejercicios de práctica

Bayesian Network Fundamentals30mBayesian Network Independencies30mOctave/Matlab installation30m

Semana2

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)

Semana3

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)

Semana4

Semana 4

22 horas para completar

Decision Making

22 horas para completar

3 videos (Total 61 minutos)

Reseñas

4.6

313 reseña

5 stars
74,66 %
4 stars
17,72 %
3 stars
5,33 %
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 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 MB30 de ago. de 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 YT14 de oct. de 2022

Top notch course! I only wish the explanations for answer choices in the quizzes/exams were more elaborate, as some of them are single sentences that don't really provide justification.

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.

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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

Acerca de este Curso

Habilidades que obtendrás