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Bayesian NetworkGraphical ModelMarkov Random Field

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강의 계획 - 이 강좌에서 배울 내용

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1

1

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Introduction and Overview

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4개 동영상 (총 35분)
4개의 동영상
Overview and Motivation19m
Distributions4m
Factors6m
1개 연습문제
Basic Definitions8m
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Bayesian Network (Directed Models)

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15개 동영상 (총 190분), 6 개의 읽기 자료, 4 개의 테스트
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
2

2

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Template Models for Bayesian Networks

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4개 동영상 (총 66분)
4개의 동영상
Temporal Models - DBNs23m
Temporal Models - HMMs12m
Plate Models20m
1개 연습문제
Template Models20m
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Structured CPDs for Bayesian Networks

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4개 동영상 (총 49분)
4개의 동영상
Tree-Structured CPDs14m
Independence of Causal Influence13m
Continuous Variables13m
2개 연습문제
Structured CPDs8m
BNs for Genetic Inheritance PA Quiz22m
3

3

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Markov Networks (Undirected Models)

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7개 동영상 (총 106분)
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
4

4

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Decision Making

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3개 동영상 (총 61분)
3개의 동영상
Utility Functions18m
Value of Perfect Information17m
2개 연습문제
Decision Theory8m
Decision Making PA Quiz18m

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PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION의 최상위 리뷰

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Probabilistic Graphical Models 특화 과정 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

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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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