课程信息
4.7
909 个评分
212 个审阅
专项课程

第 1 门课程(共 3 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
高级

高级

完成时间(小时)

完成时间大约为29 小时

建议:7 hours/week...
可选语言

英语(English)

字幕:英语(English)

您将获得的技能

Bayesian NetworkGraphical ModelMarkov Random Field
专项课程

第 1 门课程(共 3 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
高级

高级

完成时间(小时)

完成时间大约为29 小时

建议:7 hours/week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 1 小时

Introduction and Overview

This 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....
Reading
4 个视频 (总计 35 分钟), 1 个测验
Video4 个视频
Welcome!3分钟
Overview and Motivation19分钟
Distributions4分钟
Factors6分钟
Quiz1 个练习
Basic Definitions8分钟
完成时间(小时)
完成时间为 10 小时

Bayesian Network (Directed Models)

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....
Reading
15 个视频 (总计 190 分钟), 6 个阅读材料, 4 个测验
Video15 个视频
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
Reading6 个阅读材料
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
Quiz3 个练习
Bayesian Network Fundamentals6分钟
Bayesian Network Independencies10分钟
Octave/Matlab installation2分钟
2
完成时间(小时)
完成时间为 1 小时

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....
Reading
4 个视频 (总计 66 分钟), 1 个测验
Video4 个视频
Temporal Models - DBNs23分钟
Temporal Models - HMMs12分钟
Plate Models20分钟
Quiz1 个练习
Template Models20分钟
完成时间(小时)
完成时间为 11 小时

Structured CPDs for Bayesian Networks

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....
Reading
4 个视频 (总计 49 分钟), 3 个测验
Video4 个视频
Tree-Structured CPDs14分钟
Independence of Causal Influence13分钟
Continuous Variables13分钟
Quiz2 个练习
Structured CPDs8分钟
BNs for Genetic Inheritance PA Quiz22分钟
3
完成时间(小时)
完成时间为 17 小时

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....
Reading
7 个视频 (总计 106 分钟), 3 个测验
Video7 个视频
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
Quiz2 个练习
Markov Networks8分钟
Independencies Revisited6分钟
4
完成时间(小时)
完成时间为 21 小时

Decision Making

In 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....
Reading
3 个视频 (总计 61 分钟), 3 个测验
Video3 个视频
Utility Functions18分钟
Value of Perfect Information17分钟
Quiz2 个练习
Decision Theory8分钟
Decision Making PA Quiz18分钟
4.7
212 个审阅Chevron Right
职业方向

17%

完成这些课程后已开始新的职业生涯
工作福利

27%

通过此课程获得实实在在的工作福利
职业晋升

15%

加薪或升职

热门审阅

创建者 STJul 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 CMOct 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).

讲师

Avatar

Daphne Koller

Professor
School of Engineering

关于 Stanford University

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....

关于 Probabilistic Graphical Models 专项课程

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....
Probabilistic Graphical Models

常见问题

  • 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。

  • 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。

  • 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