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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Curso 3 de 3 en
Nivel avanzado
Aprox. 66 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
Habilidades que obtendrás
- Algorithms
- Expectation–Maximization (EM) Algorithm
- 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 3 de 3 en
Nivel avanzado
Aprox. 66 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), 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
16 minutos para completar
Learning: Overview
This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.
16 minutos para completar
1 video (Total 16 minutos)
1 hora para completar
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.
1 hora para completar
6 videos (Total 59 minutos)
2 horas para completar
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.
2 horas para completar
5 videos (Total 77 minutos)
21 horas para completar
Learning 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.
21 horas para completar
3 videos (Total 52 minutos)
18 horas para completar
Learning 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.
18 horas para completar
7 videos (Total 106 minutos)
22 horas para completar
Learning 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.
22 horas para completar
5 videos (Total 83 minutos)
Reseñas
- 5 stars71,42 %
- 4 stars19,72 %
- 3 stars5,44 %
- 2 stars2,72 %
- 1 star0,68 %
Principales reseñas sobre PROBABILISTIC GRAPHICAL MODELS 3: LEARNING
por SP11 de oct. de 2020
An amazing course! The assignments and quizzes can be insanely difficult espceially towards the conclusion.. Requires textbook reading and relistening to lectures to gather the nuances.
por RC6 de may. de 2020
Plz give practical assignments in Python. Matlab is not free and not many and neither myself know Matlab.
por LC27 de ago. de 2018
Great course, though with the progress of ML/DL, content seems a touch outdated. Would
por LC22 de feb. de 2019
A great course! Learned a lot. Especially the assignments are excellent! Thanks a lot.
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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