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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Probabilistic Graphical Models の専門講座スタンフォード大学（Stanford University）このコースについて
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上級レベル
約63時間で修了
英語
字幕：英語
習得するスキル
Bayesian NetworkGraphical ModelMarkov Random Field
受講生の就業成果
23%
コース終了後に新しいキャリアをスタートした
22%
コースが具体的なキャリアアップにつながった
11%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における3の1コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
上級レベル
約63時間で修了
英語
字幕：英語
提供：
スタンフォード大学（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.
シラバス - 本コースの学習内容
1時間で修了
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.
1時間で修了
4件のビデオ （合計35分）
1の練習問題
Basic Definitions8 分
10時間で修了
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.
10時間で修了
15件のビデオ （合計190分）, 6 readings, 4 quizzes
15件のビデオ
Reasoning Patterns9 分
Flow of Probabilistic Influence14 分
Conditional Independence12 分
Independencies in Bayesian Networks18 分
Naive Bayes9 分
Application - Medical Diagnosis9 分
Knowledge Engineering Example - SAMIAM14 分
Basic Operations 13 分
Moving Data Around 16 分
Computing On Data 13 分
Plotting Data 9 分
Control Statements: for, while, if statements 12 分
Vectorization 13 分
Working on and Submitting Programming Exercises 3 分
6件の学習用教材
Setting Up Your Programming Assignment Environment10 分
Installing Octave/MATLAB on Windows10 分
Installing Octave/MATLAB on Mac OS X (10.10 Yosemite and 10.9 Mavericks)10 分
Installing Octave/MATLAB on Mac OS X (10.8 Mountain Lion and Earlier)10 分
Installing Octave/MATLAB on GNU/Linux10 分
More Octave/MATLAB resources10 分
3の練習問題
Bayesian Network Fundamentals6 分
Bayesian Network Independencies10 分
Octave/Matlab installation2 分
1時間で修了
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.
1時間で修了
4件のビデオ （合計66分）
4件のビデオ
Temporal Models - DBNs23 分
Temporal Models - HMMs12 分
Plate Models20 分
1の練習問題
Template Models20 分
11時間で修了
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.
11時間で修了
4件のビデオ （合計49分）
4件のビデオ
Tree-Structured CPDs14 分
Independence of Causal Influence13 分
Continuous Variables13 分
2の練習問題
Structured CPDs8 分
BNs for Genetic Inheritance PA Quiz22 分
17時間で修了
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.
17時間で修了
7件のビデオ （合計106分）
7件のビデオ
General Gibbs Distribution15 分
Conditional Random Fields22 分
Independencies in Markov Networks4 分
I-maps and perfect maps20 分
Log-Linear Models22 分
Shared Features in Log-Linear Models8 分
2の練習問題
Markov Networks8 分
Independencies Revisited6 分
21時間で修了
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.
21時間で修了
3件のビデオ （合計61分）
2の練習問題
Decision Theory8 分
Decision Making PA Quiz18 分
レビュー
PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION からの人気レビュー
AMにより2018年11月2日
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.
BMにより2017年6月27日
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.
STにより2017年7月12日
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!!
CMにより2017年10月22日
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).
Probabilistic Graphical Models 専門講座について
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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