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 third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem.
Este curso forma parte de Programa Especializado - Probabilistic Graphical Models
Probabilistic Graphical Models 3: Learning
ofrecido por
Acerca de este Curso
4.6
183 calificaciones
Habilidades que obtendrás
AlgorithmsExpectation–Maximization (EM) AlgorithmGraphical ModelMarkov Random Field
Programa - Qué aprenderás en este curso
Semana
1Learning: Overview
This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.
1 video (Total 16 minutos)
1 video
Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)
This module contains some basic concepts from the general framework of machine learning, taken from Professor Andrew Ng's Stanford class offered on Coursera. Many of these concepts are highly relevant to the problems we'll tackle in this course.
6 videos (Total 59 minutos)
6 videos
Regularization: Cost Function 10m
Evaluating a Hypothesis 7m
Model Selection and Train Validation Test Sets 12m
Diagnosing Bias vs Variance 7m
Regularization and Bias Variance11m
Parameter Estimation in Bayesian Networks
This module discusses the simples and most basic of the learning problems in probabilistic graphical models: that of parameter estimation in a Bayesian network. We discuss maximum likelihood estimation, and the issues with it. We then discuss Bayesian estimation and how it can ameliorate these problems.
5 videos (Total 77 minutos), 2 quizzes
5 videos
Maximum Likelihood Estimation for Bayesian Networks15m
Bayesian Estimation15m
Bayesian Prediction13m
Bayesian Estimation for Bayesian Networks17m
2 ejercicios de práctica
Learning in Parametric Models18m
Bayesian Priors for BNs8m
Semana
2Learning Undirected Models
In this module, we discuss the parameter estimation problem for Markov networks - undirected graphical models. This task is considerably more complex, both conceptually and computationally, than parameter estimation for Bayesian networks, due to the issues presented by the global partition function.
3 videos (Total 52 minutos), 2 quizzes
3 videos
Maximum Likelihood for Conditional Random Fields13m
MAP Estimation for MRFs and CRFs9m
1 ejercicio de práctica
Parameter Estimation in MNs6m
Semana
3Learning BN Structure
This module discusses the problem of learning the structure of Bayesian networks. We first discuss how this problem can be formulated as an optimization problem over a space of graph structures, and what are good ways to score different structures so as to trade off fit to data and model complexity. We then talk about how the optimization problem can be solved: exactly in a few cases, approximately in most others.
7 videos (Total 106 minutos), 3 quizzes
7 videos
Likelihood Scores16m
BIC and Asymptotic Consistency11m
Bayesian Scores20m
Learning Tree Structured Networks12m
Learning General Graphs: Heuristic Search23m
Learning General Graphs: Search and Decomposability15m
2 ejercicios de práctica
Structure Scores10m
Tree Learning and Hill Climbing8m
Semana
4Learning BNs with Incomplete Data
In this module, we discuss the problem of learning models in cases where some of the variables in some of the data cases are not fully observed. We discuss why this situation is considerably more complex than the fully observable case. We then present the Expectation Maximization (EM) algorithm, which is used in a wide variety of problems.
5 videos (Total 83 minutos), 3 quizzes
5 videos
Expectation Maximization - Intro16m
Analysis of EM Algorithm11m
EM in Practice11m
Latent Variables22m
2 ejercicios de práctica
Learning with Incomplete Data8m
Expectation Maximization14m
43%
comenzó una nueva carrera después de completar estos cursos
33%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
20%
consiguió un aumento de sueldo o ascenso
Principales revisiones
por LL•Jan 30th 2018
very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.
por ZZ•Feb 14th 2017
Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.
Instructor
Daphne Koller
ProfessorSchool of Engineering
Acerca de 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.
Acerca del programa especializado Probabilistic Graphical Models
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
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
Learning Outcomes: By the end of this course, you will be able to
Compute the sufficient statistics of a data set that are necessary for learning a PGM from data
Implement both maximum likelihood and Bayesian parameter estimation for Bayesian networks
Implement maximum likelihood and MAP parameter estimation for Markov networks
Formulate a structure learning problem as a combinatorial optimization task over a space of network structure, and evaluate which scoring function is appropriate for a given situation
Utilize PGM inference algorithms in ways that support more effective parameter estimation for PGMs
Implement the Expectation Maximization (EM) algorithm for Bayesian networks
Honors track learners will get hands-on experience in implementing both EM and structure learning for tree-structured networks, and apply them to real-world tasks
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