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.
提供方
概率图模型 專項課程斯坦福大学課程信息
15,034 次近期查看
學生職業成果
43%
完成這些課程後已開始新的職業生涯
29%
通過此課程獲得實實在在的工作福利
17%
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 3 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64 小時
英語（English）
字幕：英語（English）
您將獲得的技能
AlgorithmsExpectation–Maximization (EM) AlgorithmGraphical ModelMarkov Random Field
學生職業成果
43%
完成這些課程後已開始新的職業生涯
29%
通過此課程獲得實實在在的工作福利
17%
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 3 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64 小時
英語（English）
字幕：英語（English）
提供方
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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 16 分鐘
Learning: Overview
This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.
完成時間為 16 分鐘
1 個視頻 （總計 16 分鐘）
1 個視頻
完成時間為 1 小時
Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)
This module contains some basic concepts from the general framework of machine learning, taken from Professor Andrew Ng's Stanford class offered on Coursera. Many of these concepts are highly relevant to the problems we'll tackle in this course.
完成時間為 1 小時
6 個視頻 （總計 59 分鐘）
6 個視頻
Regularization: Cost Function 10分鐘
Evaluating a Hypothesis 7分鐘
Model Selection and Train Validation Test Sets 12分鐘
Diagnosing Bias vs Variance 7分鐘
Regularization and Bias Variance11分鐘
完成時間為 2 小時
Parameter Estimation in Bayesian Networks
This module discusses the simples and most basic of the learning problems in probabilistic graphical models: that of parameter estimation in a Bayesian network. We discuss maximum likelihood estimation, and the issues with it. We then discuss Bayesian estimation and how it can ameliorate these problems.
完成時間為 2 小時
5 個視頻 （總計 77 分鐘）
5 個視頻
Maximum Likelihood Estimation for Bayesian Networks15分鐘
Bayesian Estimation15分鐘
Bayesian Prediction13分鐘
Bayesian Estimation for Bayesian Networks17分鐘
2 個練習
Learning in Parametric Models18分鐘
Bayesian Priors for BNs8分鐘
完成時間為 21 小時
Learning Undirected Models
In this module, we discuss the parameter estimation problem for Markov networks - undirected graphical models. This task is considerably more complex, both conceptually and computationally, than parameter estimation for Bayesian networks, due to the issues presented by the global partition function.
完成時間為 21 小時
3 個視頻 （總計 52 分鐘）
3 個視頻
Maximum Likelihood for Conditional Random Fields13分鐘
MAP Estimation for MRFs and CRFs9分鐘
1 個練習
Parameter Estimation in MNs6分鐘
完成時間為 17 小時
Learning BN Structure
This module discusses the problem of learning the structure of Bayesian networks. We first discuss how this problem can be formulated as an optimization problem over a space of graph structures, and what are good ways to score different structures so as to trade off fit to data and model complexity. We then talk about how the optimization problem can be solved: exactly in a few cases, approximately in most others.
完成時間為 17 小時
7 個視頻 （總計 106 分鐘）
7 個視頻
Likelihood Scores16分鐘
BIC and Asymptotic Consistency11分鐘
Bayesian Scores20分鐘
Learning Tree Structured Networks12分鐘
Learning General Graphs: Heuristic Search23分鐘
Learning General Graphs: Search and Decomposability15分鐘
2 個練習
Structure Scores10分鐘
Tree Learning and Hill Climbing8分鐘
完成時間為 22 小時
Learning BNs with Incomplete Data
In this module, we discuss the problem of learning models in cases where some of the variables in some of the data cases are not fully observed. We discuss why this situation is considerably more complex than the fully observable case. We then present the Expectation Maximization (EM) algorithm, which is used in a wide variety of problems.
完成時間為 22 小時
5 個視頻 （總計 83 分鐘）
5 個視頻
Expectation Maximization - Intro16分鐘
Analysis of EM Algorithm11分鐘
EM in Practice11分鐘
Latent Variables22分鐘
2 個練習
Learning with Incomplete Data8分鐘
Expectation Maximization14分鐘
審閱
來自PROBABILISTIC GRAPHICAL MODELS 3: LEARNING的熱門評論
由 LL 提供2018年1月29日
very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.
由 ZZ 提供2017年2月13日
Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.
由 MV 提供2020年4月29日
Great course, especially the programming assignments. Textbook is pretty much necessary for some quizzes, definitely for the final one.
由 JR 提供2018年1月28日
Great course! It is pretty difficult - be prepared to study. Leave plenty of time before the final exam.
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.
常見問題
我什么时候能够访问课程视频和作业？
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.
我订阅此专项课程后会得到什么？
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。
退款政策是如何规定的？
如果订阅，您可以获得 7 天免费试听，在此期间，您可以取消课程，无需支付任何罚金。在此之后，我们不会退款，但您可以随时取消订阅。请阅读我们完整的退款政策。
有助学金吗？
是的，Coursera 可以为无法承担费用的学生提供助学金。通过点击左侧“注册”按钮下的“助学金”链接可以申请助学金。您可以根据屏幕提示完成申请，申请获批后会收到通知。您需要针对专项课程中的每一门课程完成上述步骤，包括毕业项目。了解更多。
Learning Outcomes: By the end of this course, you will be able to
Compute the sufficient statistics of a data set that are necessary for learning a PGM from data
Implement both maximum likelihood and Bayesian parameter estimation for Bayesian networks
Implement maximum likelihood and MAP parameter estimation for Markov networks
Formulate a structure learning problem as a combinatorial optimization task over a space of network structure, and evaluate which scoring function is appropriate for a given situation
Utilize PGM inference algorithms in ways that support more effective parameter estimation for PGMs
Implement the Expectation Maximization (EM) algorithm for Bayesian networks
Honors track learners will get hands-on experience in implementing both EM and structure learning for tree-structured networks, and apply them to real-world tasks
完成课程后，我会获得大学学分吗？
此课程不提供大学学分，但部分大学可能会选择接受课程证书作为学分。查看您的合作院校，了解详情。Coursera 上的在线学位和 Mastertrack™ 证书提供获得大学学分的机会。
還有其他問題嗎？請訪問 學生幫助中心。
