課程信息
4.7
982 個評分
221 個審閱

第 1 門課程(共 3 門)

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高級

完成時間大約為29 小時

建議:7 hours/week...

英語(English)

字幕:英語(English)

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Bayesian NetworkGraphical ModelMarkov Random Field

第 1 門課程(共 3 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

高級

完成時間大約為29 小時

建議:7 hours/week...

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 1 小時

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....
4 個視頻 (總計 35 分鐘), 1 個測驗
4 個視頻
Welcome!3分鐘
Overview and Motivation19分鐘
Distributions4分鐘
Factors6分鐘
1 個練習
Basic Definitions8分鐘
完成時間為 10 小時

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....
15 個視頻 (總計 190 分鐘), 6 個閱讀材料, 4 個測驗
15 個視頻
Reasoning Patterns9分鐘
Flow of Probabilistic Influence14分鐘
Conditional Independence12分鐘
Independencies in Bayesian Networks18分鐘
Naive Bayes9分鐘
Application - Medical Diagnosis9分鐘
Knowledge Engineering Example - SAMIAM14分鐘
Basic Operations 13分鐘
Moving Data Around 16分鐘
Computing On Data 13分鐘
Plotting Data 9分鐘
Control Statements: for, while, if statements 12分鐘
Vectorization 13分鐘
Working on and Submitting Programming Exercises 3分鐘
6 個閱讀材料
Setting Up Your Programming Assignment Environment10分鐘
Installing Octave/MATLAB on Windows10分鐘
Installing Octave/MATLAB on Mac OS X (10.10 Yosemite and 10.9 Mavericks)10分鐘
Installing Octave/MATLAB on Mac OS X (10.8 Mountain Lion and Earlier)10分鐘
Installing Octave/MATLAB on GNU/Linux10分鐘
More Octave/MATLAB resources10分鐘
3 個練習
Bayesian Network Fundamentals6分鐘
Bayesian Network Independencies10分鐘
Octave/Matlab installation2分鐘
2
完成時間為 1 小時

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....
4 個視頻 (總計 66 分鐘), 1 個測驗
4 個視頻
Temporal Models - DBNs23分鐘
Temporal Models - HMMs12分鐘
Plate Models20分鐘
1 個練習
Template Models20分鐘
完成時間為 11 小時

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....
4 個視頻 (總計 49 分鐘), 3 個測驗
4 個視頻
Tree-Structured CPDs14分鐘
Independence of Causal Influence13分鐘
Continuous Variables13分鐘
2 個練習
Structured CPDs8分鐘
BNs for Genetic Inheritance PA Quiz22分鐘
3
完成時間為 17 小時

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....
7 個視頻 (總計 106 分鐘), 3 個測驗
7 個視頻
General Gibbs Distribution15分鐘
Conditional Random Fields22分鐘
Independencies in Markov Networks4分鐘
I-maps and perfect maps20分鐘
Log-Linear Models22分鐘
Shared Features in Log-Linear Models8分鐘
2 個練習
Markov Networks8分鐘
Independencies Revisited6分鐘
4
完成時間為 21 小時

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....
3 個視頻 (總計 61 分鐘), 3 個測驗
3 個視頻
Utility Functions18分鐘
Value of Perfect Information17分鐘
2 個練習
Decision Theory8分鐘
Decision Making PA Quiz18分鐘
4.7
221 個審閱Chevron Right

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完成這些課程後已開始新的職業生涯

26%

通過此課程獲得實實在在的工作福利

13%

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創建者 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!!

創建者 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).

講師

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Daphne Koller

Professor
School of Engineering

關於 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....

關於 概率图模型 專項課程

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....
概率图模型

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  • 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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