课程信息
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第 1 门课程(共 3 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

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高级

完成时间大约为30 小时

英语(English)

字幕:英语(English)

您将获得的技能

Bayesian NetworkGraphical ModelMarkov Random Field

第 1 门课程(共 3 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

高级

完成时间大约为30 小时

英语(English)

字幕:英语(English)

学习Course的学生是

  • Data Scientists
  • Machine Learning Engineers
  • Biostatisticians
  • Research Assistants
  • Researchers

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 1 小时

Introduction and Overview

4 个视频 (总计 35 分钟), 1 个测验
4 个视频
Welcome!3分钟
Overview and Motivation19分钟
Distributions4分钟
Factors6分钟
1 个练习
Basic Definitions8分钟
完成时间为 10 小时

Bayesian Network (Directed Models)

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
2
完成时间为 1 小时

Template Models for Bayesian Networks

4 个视频 (总计 66 分钟), 1 个测验
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

4 个视频 (总计 49 分钟), 3 个测验
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
3
完成时间为 17 小时

Markov Networks (Undirected Models)

7 个视频 (总计 106 分钟), 3 个测验
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
4
完成时间为 21 小时

Decision Making

3 个视频 (总计 61 分钟), 3 个测验
3 个视频
Utility Functions18分钟
Value of Perfect Information17分钟
2 个练习
Decision Theory8分钟
Decision Making PA Quiz18分钟
4.7
246 个审阅Chevron Right

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

来自Probabilistic Graphical Models 1: Representation的热门评论

创建者 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!!

创建者 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).

讲师

Avatar

Daphne Koller

Professor
School of Engineering

关于 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....

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....
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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