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

4.6
estrellas
478 calificaciones

Daphne Koller

23.668 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 second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.

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Curso 2 de 3 en

Programa especializado: modelos gráficos de probabilidades

Nivel avanzado

Aprox. 38 horas para completar

Inglés (English)

Habilidades que obtendrás

Inference
Gibbs Sampling
Markov Chain Monte Carlo (MCMC)
Belief Propagation

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 2 de 3 en

Programa especializado: modelos gráficos de probabilidades

Nivel avanzado

Aprox. 38 horas para completar

Inglés (English)

Instructor

Calificación del instructor

4,81/5 (16 calificaciones)

Daphne Koller

Professor

School of Engineering

88.422 alumnos

3 cursos

Ofrecido por

Universidad de Stanford

Programa - Qué aprenderás en este curso

Semana

Semana 1

25 minutos para completar

Inference Overview

25 minutos para completar

2 videos (Total 25 minutos)

1 hora para completar

Variable Elimination

1 hora para completar

4 videos (Total 56 minutos)

Semana

Semana 2

18 horas para completar

Belief Propagation Algorithms

This module describes an alternative view of exact inference in graphical models: that of message passing between clusters each of which encodes a factor over a subset of variables. This framework provides a basis for a variety of exact and approximate inference algorithms. We focus here on the basic framework and on its instantiation in the exact case of clique tree propagation. An optional lesson describes the loopy belief propagation (LBP) algorithm and its properties.

18 horas para completar

9 videos (Total 150 minutos)

Semana

Semana 3

2 horas para completar

MAP Algorithms

This module describes algorithms for finding the most likely assignment for a distribution encoded as a PGM (a task known as MAP inference). We describe message passing algorithms, which are very similar to the algorithms for computing conditional probabilities, except that we need to also consider how to decode the results to construct a single assignment. In an optional module, we describe a few other algorithms that are able to use very different techniques by exploiting the combinatorial optimization nature of the MAP task.

2 horas para completar

5 videos (Total 74 minutos)

Semana

Semana 4

15 horas para completar

Sampling Methods

15 horas para completar

5 videos (Total 100 minutos)

1 hora para completar

Inference in Temporal Models

1 hora para completar

1 video (Total 20 minutos)

Reseñas

4.6

74 reseña

5 stars
71,33 %
4 stars
21,12 %
3 stars
5,23 %
2 stars
1,04 %
1 star
1,25 %

Principales reseñas sobre PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE

por AL19 de ago. de 2019

I have clearly learnt a lot during this course. Even though some things should be updated and maybe completed, I would definitely recommend it to anyone whose interest lies in PGMs.

por GV27 de nov. de 2017

great course, though really advanced. would like a bit more examples especially regarding the coding. worth it overally

por LC2 de feb. de 2019

Very great course! A lot of things have been learnt. The lectures, quiz and assignments clear up all key concepts. Especially, assignments are wonderful!

por MP19 de ene. de 2021

Great course! Course has filled gaps in my knowledge from statistics and similar sciences.

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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 take a given PGM and
Execute the basic steps of a variable elimination or message passing algorithm
Understand how properties of the graph structure influence the complexity of exact inference, and thereby estimate whether exact inference is likely to be feasible
Go through the basic steps of an MCMC algorithm, both Gibbs sampling and Metropolis Hastings
Understand how properties of the PGM influence the efficacy of sampling methods, and thereby estimate whether MCMC algorithms are likely to be effective
Design Metropolis Hastings proposal distributions that are more likely to give good results
Compute a MAP assignment by exact inference
Honors track learners will be able to implement message passing algorithms and MCMC algorithms, and apply them to a real world problem

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Probabilistic Graphical Models 2: Inference

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Habilidades que obtendrás