課程信息

21,420 次近期查看

學生職業成果

50%

完成這些課程後已開始新的職業生涯

20%

通過此課程獲得實實在在的工作福利

20%

加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始,按照自己的計劃學習。
第 2 門課程(共 3 門)
可靈活調整截止日期
根據您的日程表重置截止日期。
高級
完成時間大約為36 小時
英語(English)
字幕:英語(English)

您將獲得的技能

InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation

學生職業成果

50%

完成這些課程後已開始新的職業生涯

20%

通過此課程獲得實實在在的工作福利

20%

加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始,按照自己的計劃學習。
第 2 門課程(共 3 門)
可靈活調整截止日期
根據您的日程表重置截止日期。
高級
完成時間大約為36 小時
英語(English)
字幕:英語(English)

提供方

斯坦福大学 徽標

斯坦福大学

教學大綱 - 您將從這門課程中學到什麼

1

1

完成時間為 25 分鐘

Inference Overview

完成時間為 25 分鐘
2 個視頻 (總計 25 分鐘)
2 個視頻
Overview: MAP Inference9分鐘
完成時間為 1 小時

Variable Elimination

完成時間為 1 小時
4 個視頻 (總計 56 分鐘)
4 個視頻
Complexity of Variable Elimination12分鐘
Graph-Based Perspective on Variable Elimination15分鐘
Finding Elimination Orderings11分鐘
1 個練習
Variable Elimination18分鐘
2

2

完成時間為 18 小時

Belief Propagation Algorithms

完成時間為 18 小時
9 個視頻 (總計 150 分鐘)
9 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
2 個練習
Message Passing in Cluster Graphs10分鐘
Clique Tree Algorithm10分鐘
3

3

完成時間為 1 小時

MAP Algorithms

完成時間為 1 小時
5 個視頻 (總計 74 分鐘)
5 個視頻
Finding a MAP Assignment3分鐘
Tractable MAP Problems15分鐘
Dual Decomposition - Intuition17分鐘
Dual Decomposition - Algorithm16分鐘
1 個練習
MAP Message Passing4分鐘
4

4

完成時間為 14 小時

Sampling Methods

完成時間為 14 小時
5 個視頻 (總計 100 分鐘)
5 個視頻
Markov Chain Monte Carlo14分鐘
Using a Markov Chain15分鐘
Gibbs Sampling19分鐘
Metropolis Hastings Algorithm27分鐘
2 個練習
Sampling Methods14分鐘
Sampling Methods PA Quiz8分鐘
完成時間為 26 分鐘

Inference in Temporal Models

完成時間為 26 分鐘
1 個視頻 (總計 20 分鐘)
1 個視頻
1 個練習
Inference in Temporal Models6分鐘

審閱

來自PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE的熱門評論

查看所有評論

關於 概率图模型 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....
概率图模型

常見問題

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • 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

還有其他問題嗎?請訪問 學生幫助中心