課程信息
4.6
288 個評分
47 個審閱
專項課程

第 2 門課程(共 3 門),位於

100% 在線

100% 在線

立即開始,按照自己的計劃學習。
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可靈活調整截止日期

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高級

高級

完成時間(小時)

完成時間大約為23 小時

建議:7 hours/week...
可選語言

英語(English)

字幕:英語(English)...

您將獲得的技能

InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation
專項課程

第 2 門課程(共 3 門),位於

100% 在線

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

可靈活調整截止日期

根據您的日程表重置截止日期。
高級

高級

完成時間(小時)

完成時間大約為23 小時

建議:7 hours/week...
可選語言

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(小時)
完成時間為 25 分鐘

Inference Overview

This module provides a high-level overview of the main types of inference tasks typically encountered in graphical models: conditional probability queries, and finding the most likely assignment (MAP inference)....
Reading
2 個視頻(共 25 分鐘)
Video2 個視頻
Overview: MAP Inference9分鐘
完成時間(小時)
完成時間為 1 小時

Variable Elimination

This module presents the simplest algorithm for exact inference in graphical models: variable elimination. We describe the algorithm, and analyze its complexity in terms of properties of the graph structure....
Reading
4 個視頻(共 56 分鐘), 1 個測驗
Video4 個視頻
Complexity of Variable Elimination12分鐘
Graph-Based Perspective on Variable Elimination15分鐘
Finding Elimination Orderings11分鐘
Quiz1 個練習
Variable Elimination18分鐘
2
完成時間(小時)
完成時間為 18 小時

Belief Propagation Algorithms

This module describes an alternative view of exact inference in graphical models: that of message passing between clusters each of which encodes a factor over a subset of variables. This framework provides a basis for a variety of exact and approximate inference algorithms. We focus here on the basic framework and on its instantiation in the exact case of clique tree propagation. An optional lesson describes the loopy belief propagation (LBP) algorithm and its properties....
Reading
9 個視頻(共 150 分鐘), 3 個測驗
Video9 個視頻
Properties of Cluster Graphs15分鐘
Properties of Belief Propagation9分鐘
Clique Tree Algorithm - Correctness18分鐘
Clique Tree Algorithm - Computation16分鐘
Clique Trees and Independence15分鐘
Clique Trees and VE16分鐘
BP In Practice15分鐘
Loopy BP and Message Decoding21分鐘
Quiz2 個練習
Message Passing in Cluster Graphs10分鐘
Clique Tree Algorithm10分鐘
3
完成時間(小時)
完成時間為 1 小時

MAP Algorithms

This module describes algorithms for finding the most likely assignment for a distribution encoded as a PGM (a task known as MAP inference). We describe message passing algorithms, which are very similar to the algorithms for computing conditional probabilities, except that we need to also consider how to decode the results to construct a single assignment. In an optional module, we describe a few other algorithms that are able to use very different techniques by exploiting the combinatorial optimization nature of the MAP task....
Reading
5 個視頻(共 74 分鐘), 1 個測驗
Video5 個視頻
Finding a MAP Assignment3分鐘
Tractable MAP Problems15分鐘
Dual Decomposition - Intuition17分鐘
Dual Decomposition - Algorithm16分鐘
Quiz1 個練習
MAP Message Passing4分鐘
4
完成時間(小時)
完成時間為 14 小時

Sampling Methods

In this module, we discuss a class of algorithms that uses random sampling to provide approximate answers to conditional probability queries. Most commonly used among these is the class of Markov Chain Monte Carlo (MCMC) algorithms, which includes the simple Gibbs sampling algorithm, as well as a family of methods known as Metropolis-Hastings....
Reading
5 個視頻(共 100 分鐘), 3 個測驗
Video5 個視頻
Markov Chain Monte Carlo14分鐘
Using a Markov Chain15分鐘
Gibbs Sampling19分鐘
Metropolis Hastings Algorithm27分鐘
Quiz2 個練習
Sampling Methods14分鐘
Sampling Methods PA Quiz8分鐘
完成時間(小時)
完成時間為 26 分鐘

Inference in Temporal Models

In this brief lesson, we discuss some of the complexities of applying some of the exact or approximate inference algorithms that we learned earlier in this course to dynamic Bayesian networks....
Reading
1 個視頻(共 20 分鐘), 1 個測驗
Video1 個視頻
Quiz1 個練習
Inference in Temporal Models6分鐘
4.6
47 個審閱Chevron Right
職業方向

50%

完成這些課程後已開始新的職業生涯
工作福利

83%

通過此課程獲得實實在在的工作福利
職業晉升

33%

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熱門審閱

創建者 LLMar 12th 2017

Thanks a lot for professor D.K.'s great course for PGM inference part. Really a very good starting point for PGM model and preparation for learning part.

創建者 YPMay 29th 2017

I learned pretty much from this course. It answered my quandaries from the representation course, and as well deepened my understanding of PGM.

講師

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Daphne Koller

Professor
School of Engineering

關於 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....

關於 Probabilistic Graphical Models 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....
Probabilistic Graphical Models

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  • Execute the basic steps of a variable elimination or message passing algorithm

    Understand how properties of the graph structure influence the complexity of exact inference, and thereby estimate whether exact inference is likely to be feasible

    Go through the basic steps of an MCMC algorithm, both Gibbs sampling and Metropolis Hastings

    Understand how properties of the PGM influence the efficacy of sampling methods, and thereby estimate whether MCMC algorithms are likely to be effective

    Design Metropolis Hastings proposal distributions that are more likely to give good results

    Compute a MAP assignment by exact inference

    Honors track learners will be able to implement message passing algorithms and MCMC algorithms, and apply them to a real world problem

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