本课程是 Probabilistic Graphical Models 专项课程 专项课程的一部分

提供方

Probabilistic Graphical Models 专项课程

Stanford University

课程信息

4.7

909 个评分

•

212 个审阅

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.

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

建议：7 hours/week...

字幕：英语（English）

Bayesian NetworkGraphical ModelMarkov Random Field

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

建议：7 hours/week...

字幕：英语（English）

周

1This module provides an overall introduction to probabilistic graphical models, and defines a few of the key concepts that will be used later in the course....

4 个视频 （总计 35 分钟）, 1 个测验

Basic Definitions8分钟

In this module, we define the Bayesian network representation and its semantics. We also analyze the relationship between the graph structure and the independence properties of a distribution represented over that graph. Finally, we give some practical tips on how to model a real-world situation as a Bayesian network....

15 个视频 （总计 190 分钟）, 6 个阅读材料, 4 个测验

Reasoning Patterns9分钟

Flow of Probabilistic Influence14分钟

Conditional Independence12分钟

Independencies in Bayesian Networks18分钟

Naive Bayes9分钟

Application - Medical Diagnosis9分钟

Knowledge Engineering Example - SAMIAM14分钟

Basic Operations 13分钟

Moving Data Around 16分钟

Computing On Data 13分钟

Plotting Data 9分钟

Control Statements: for, while, if statements 12分钟

Vectorization 13分钟

Working on and Submitting Programming Exercises 3分钟

Setting Up Your Programming Assignment Environment10分钟

Installing Octave/MATLAB on Windows10分钟

Installing Octave/MATLAB on Mac OS X (10.10 Yosemite and 10.9 Mavericks)10分钟

Installing Octave/MATLAB on Mac OS X (10.8 Mountain Lion and Earlier)10分钟

Installing Octave/MATLAB on GNU/Linux10分钟

More Octave/MATLAB resources10分钟

Bayesian Network Fundamentals6分钟

Bayesian Network Independencies10分钟

Octave/Matlab installation2分钟

周

2In 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....

4 个视频 （总计 66 分钟）, 1 个测验

Temporal Models - DBNs23分钟

Temporal Models - HMMs12分钟

Plate Models20分钟

Template Models20分钟

A table-based representation of a CPD in a Bayesian network has a size that grows exponentially in the number of parents. There are a variety of other form of CPD that exploit some type of structure in the dependency model to allow for a much more compact representation. Here we describe a number of the ones most commonly used in practice....

4 个视频 （总计 49 分钟）, 3 个测验

Tree-Structured CPDs14分钟

Independence of Causal Influence13分钟

Continuous Variables13分钟

Structured CPDs8分钟

BNs for Genetic Inheritance PA Quiz22分钟

周

3In 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....

7 个视频 （总计 106 分钟）, 3 个测验

General Gibbs Distribution15分钟

Conditional Random Fields22分钟

Independencies in Markov Networks4分钟

I-maps and perfect maps20分钟

Log-Linear Models22分钟

Shared Features in Log-Linear Models8分钟

Markov Networks8分钟

Independencies Revisited6分钟

周

4In this module, we discuss the task of decision making under uncertainty. We describe the framework of decision theory, including some aspects of utility functions. We then talk about how decision making scenarios can be encoded as a graphical model called an Influence Diagram, and how such models provide insight both into decision making and the value of information gathering....

3 个视频 （总计 61 分钟）, 3 个测验

Decision Theory8分钟

Decision Making PA Quiz18分钟

4.7

212 个审阅完成这些课程后已开始新的职业生涯

通过此课程获得实实在在的工作福利

加薪或升职

创建者 ST•Jul 13th 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!!

创建者 CM•Oct 23rd 2017

The course was deep, and well-taught. This is not a spoon-feeding course like some others. The only downside were some "mechanical" problems (e.g. code submission didn't work for me).

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....

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....

我什么时候能够访问课程视频和作业？

注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。

我订阅此专项课程后会得到什么？

您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。

退款政策是如何规定的？

有助学金吗？

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

还有其他问题吗？请访问 学生帮助中心。