课程信息

20,366 次近期查看

学生职业成果

50%

完成这些课程后已开始新的职业生涯

20%

通过此课程获得实实在在的工作福利

20%

加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 2 门课程(共 3 门)
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36 小时
英语(English)
字幕:英语(English)

您将获得的技能

InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation

学生职业成果

50%

完成这些课程后已开始新的职业生涯

20%

通过此课程获得实实在在的工作福利

20%

加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 2 门课程(共 3 门)
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36 小时
英语(English)
字幕:英语(English)

提供方

斯坦福大学 徽标

斯坦福大学

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 25 分钟

Inference Overview

完成时间为 25 分钟
2 个视频 (总计 25 分钟)
2 个视频
Overview: MAP Inference9分钟
完成时间为 1 小时

Variable Elimination

完成时间为 1 小时
4 个视频 (总计 56 分钟)
4 个视频
Complexity of Variable Elimination12分钟
Graph-Based Perspective on Variable Elimination15分钟
Finding Elimination Orderings11分钟
1 个练习
Variable Elimination18分钟
2

2

完成时间为 18 小时

Belief Propagation Algorithms

完成时间为 18 小时
9 个视频 (总计 150 分钟)
9 个视频
Properties of Cluster Graphs15分钟
Properties of Belief Propagation9分钟
Clique Tree Algorithm - Correctness18分钟
Clique Tree Algorithm - Computation16分钟
Clique Trees and Independence15分钟
Clique Trees and VE16分钟
BP In Practice15分钟
Loopy BP and Message Decoding21分钟
2 个练习
Message Passing in Cluster Graphs10分钟
Clique Tree Algorithm10分钟
3

3

完成时间为 1 小时

MAP Algorithms

完成时间为 1 小时
5 个视频 (总计 74 分钟)
5 个视频
Finding a MAP Assignment3分钟
Tractable MAP Problems15分钟
Dual Decomposition - Intuition17分钟
Dual Decomposition - Algorithm16分钟
1 个练习
MAP Message Passing4分钟
4

4

完成时间为 14 小时

Sampling Methods

完成时间为 14 小时
5 个视频 (总计 100 分钟)
5 个视频
Markov Chain Monte Carlo14分钟
Using a Markov Chain15分钟
Gibbs Sampling19分钟
Metropolis Hastings Algorithm27分钟
2 个练习
Sampling Methods14分钟
Sampling Methods PA Quiz8分钟
完成时间为 26 分钟

Inference in Temporal Models

完成时间为 26 分钟
1 个视频 (总计 20 分钟)
1 个视频
1 个练习
Inference in Temporal Models6分钟

审阅

来自PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE的热门评论

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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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  • 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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