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다음 전문 분야의 3개 강좌 중 1번째 강좌:

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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

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일정에 따라 마감일을 재설정합니다.

고급 단계


자막: 영어

귀하가 습득할 기술

Bayesian NetworkGraphical ModelMarkov Random Field

다음 전문 분야의 3개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계


자막: 영어

강의 계획 - 이 강좌에서 배울 내용

완료하는 데 1시간 필요

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 videos (Total 35 min), 1 quiz
4개의 동영상
Overview and Motivation19m
1개 연습문제
Basic Definitions8m
완료하는 데 10시간 필요

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 videos (Total 190 min), 6 readings, 4 quizzes
15개의 동영상
Reasoning Patterns9m
Flow of Probabilistic Influence14m
Conditional Independence12m
Independencies in Bayesian Networks18m
Naive Bayes9m
Application - Medical Diagnosis9m
Knowledge Engineering Example - SAMIAM14m
Basic Operations 13m
Moving Data Around 16m
Computing On Data 13m
Plotting Data 9m
Control Statements: for, while, if statements 12m
Vectorization 13m
Working on and Submitting Programming Exercises 3m
6개의 읽기 자료
Setting Up Your Programming Assignment Environment10m
Installing Octave/MATLAB on Windows10m
Installing Octave/MATLAB on Mac OS X (10.10 Yosemite and 10.9 Mavericks)10m
Installing Octave/MATLAB on Mac OS X (10.8 Mountain Lion and Earlier)10m
Installing Octave/MATLAB on GNU/Linux10m
More Octave/MATLAB resources10m
3개 연습문제
Bayesian Network Fundamentals6m
Bayesian Network Independencies10m
Octave/Matlab installation2m
완료하는 데 1시간 필요

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 videos (Total 66 min), 1 quiz
4개의 동영상
Temporal Models - DBNs23m
Temporal Models - HMMs12m
Plate Models20m
1개 연습문제
Template Models20m
완료하는 데 11시간 필요

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 videos (Total 49 min), 3 quizzes
4개의 동영상
Tree-Structured CPDs14m
Independence of Causal Influence13m
Continuous Variables13m
2개 연습문제
Structured CPDs8m
BNs for Genetic Inheritance PA Quiz22m
완료하는 데 17시간 필요

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 videos (Total 106 min), 3 quizzes
7개의 동영상
General Gibbs Distribution15m
Conditional Random Fields22m
Independencies in Markov Networks4m
I-maps and perfect maps20m
Log-Linear Models22m
Shared Features in Log-Linear Models8m
2개 연습문제
Markov Networks8m
Independencies Revisited6m
완료하는 데 21시간 필요

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 videos (Total 61 min), 3 quizzes
3개의 동영상
Utility Functions18m
Value of Perfect Information17m
2개 연습문제
Decision Theory8m
Decision Making PA Quiz18m
233개의 리뷰Chevron Right


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Probabilistic Graphical Models 1: Representation의 최상위 리뷰

대학: 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!!

대학: 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).



Daphne Koller

School of Engineering

스탠퍼드 대학교 정보

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 전문 분야 정보

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

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

  • 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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