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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Bayesian NetworkGraphical ModelMarkov Random Field
学生职业成果
23%
完成这些课程后已开始新的职业生涯
22%
通过此课程获得实实在在的工作福利
11%
加薪或升职
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第 1 门课程（共 3 门）
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高级
完成时间大约为30 小时
英语（English）
字幕：英语（English）
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斯坦福大学
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.
教学大纲 - 您将从这门课程中学到什么
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.
完成时间为 1 小时
4 个视频 （总计 35 分钟）
1 个练习
Basic Definitions8分钟
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.
完成时间为 10 小时
15 个视频 （总计 190 分钟）, 6 个阅读材料, 4 个测验
15 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
6 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
3 个练习
Bayesian Network Fundamentals6分钟
Bayesian Network Independencies10分钟
Octave/Matlab installation2分钟
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.
完成时间为 1 小时
4 个视频 （总计 66 分钟）
4 个视频
Temporal Models - DBNs23分钟
Temporal Models - HMMs12分钟
Plate Models20分钟
1 个练习
Template Models20分钟
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.
完成时间为 11 小时
4 个视频 （总计 49 分钟）
4 个视频
Tree-Structured CPDs14分钟
Independence of Causal Influence13分钟
Continuous Variables13分钟
2 个练习
Structured CPDs8分钟
BNs for Genetic Inheritance PA Quiz22分钟
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.
完成时间为 17 小时
7 个视频 （总计 106 分钟）
7 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
2 个练习
Markov Networks8分钟
Independencies Revisited6分钟
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.
完成时间为 21 小时
3 个视频 （总计 61 分钟）
2 个练习
Decision Theory8分钟
Decision Making PA Quiz18分钟
审阅
4.7
来自PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION的热门评论
由 AM 提供Nov 2, 2018
Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.
由 BM 提供Jun 27, 2017
The lecture was a bit too compact and unsystematic. However, if you also do a lot of reading of the textbook, you can learn a lot. Besides, the Quiz and Programming task are of high qualities.
由 ST 提供Jul 12, 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!!
由 CM 提供Oct 22, 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).
由 MB 提供Aug 30, 2018
Excellent course, the effort of the instructor is well reflected in the content and the exercices. A must for every serious student on (decision theory or markov random fields tasks.
由 AK 提供Nov 12, 2016
Superb exposition. Makes me want to continue learning till the very end of this course. Very intuitive explanations. Plan to complete all courses offered in this specialization.
由 DP 提供Jun 17, 2017
I have Actually Earned Three Years of my life (at least) and one possible patent because of this course.\n\nThank You Daphne Mam. God Bless Everybody Associated with it.
由 CB 提供Jul 16, 2017
learned a lot. lectures were easy to follow and the textbook was able to more fully explain things when I needed it. looking forward to the next course in the series.
关于 概率图模型 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.
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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
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