课程信息
4.6
292 个评分
48 个审阅
专项课程

第 2 门课程(共 3 门)

100% 在线

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

可灵活调整截止日期

根据您的日程表重置截止日期。
高级

高级

完成时间(小时)

完成时间大约为23 小时

建议:7 hours/week...
可选语言

英语(English)

字幕:英语(English)

您将获得的技能

InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation
专项课程

第 2 门课程(共 3 门)

100% 在线

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

可灵活调整截止日期

根据您的日程表重置截止日期。
高级

高级

完成时间(小时)

完成时间大约为23 小时

建议:7 hours/week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 25 分钟

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 个视频 (总计 25 分钟)
Video2 个视频
Overview: MAP Inference9分钟
完成时间(小时)
完成时间为 1 小时

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 个视频 (总计 56 分钟), 1 个测验
Video4 个视频
Complexity of Variable Elimination12分钟
Graph-Based Perspective on Variable Elimination15分钟
Finding Elimination Orderings11分钟
Quiz1 个练习
Variable Elimination18分钟
2
完成时间(小时)
完成时间为 18 小时

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 个视频 (总计 150 分钟), 3 个测验
Video9 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
Quiz2 个练习
Message Passing in Cluster Graphs10分钟
Clique Tree Algorithm10分钟
3
完成时间(小时)
完成时间为 1 小时

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 个视频 (总计 74 分钟), 1 个测验
Video5 个视频
Finding a MAP Assignment3分钟
Tractable MAP Problems15分钟
Dual Decomposition - Intuition17分钟
Dual Decomposition - Algorithm16分钟
Quiz1 个练习
MAP Message Passing4分钟
4
完成时间(小时)
完成时间为 14 小时

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 个视频 (总计 100 分钟), 3 个测验
Video5 个视频
Markov Chain Monte Carlo14分钟
Using a Markov Chain15分钟
Gibbs Sampling19分钟
Metropolis Hastings Algorithm27分钟
Quiz2 个练习
Sampling Methods14分钟
Sampling Methods PA Quiz8分钟
完成时间(小时)
完成时间为 26 分钟

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 个视频 (总计 20 分钟), 1 个测验
Video1 个视频
Quiz1 个练习
Inference in Temporal Models6分钟
4.6
48 个审阅Chevron Right
职业方向

50%

完成这些课程后已开始新的职业生涯
工作福利

33%

通过此课程获得实实在在的工作福利
职业晋升

33%

加薪或升职

热门审阅

创建者 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.

创建者 JLApr 9th 2018

I would have like to complete the honors assignments, unfortunately, I'm not fluent in Matlab. Otherwise, great course!

讲师

Avatar

Daphne Koller

Professor
School of Engineering

关于 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....

关于 Probabilistic Graphical Models 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....
Probabilistic Graphical Models

常见问题

  • 注册以便获得证书后,您将有权访问所有视频、测验和编程作业(如果适用)。只有在您的班次开课之后,才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程,可能无法访问某些作业。

  • 您注册课程后,将有权访问专项课程中的所有课程,并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中,您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容,可以免费旁听课程。

  • 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

还有其他问题吗?请访问 学生帮助中心