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.
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概率图模型 專項課程斯坦福大学課程信息
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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）
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高級
完成時間大約為66 小時
英語（English）
字幕：法語（French）, （歐洲人講的）葡萄牙語, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
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66 小時
英語（English）
字幕：法語（French）, （歐洲人講的）葡萄牙語, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
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）
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）
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）
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）
完成時間為 18 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 18 小時
7 個視頻 （總計 106 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）
審閱
來自PROBABILISTIC GRAPHICAL MODELS 3: LEARNING的熱門評論
由 SP 提供2020年10月11日
An amazing course! The assignments and quizzes can be insanely difficult espceially towards the conclusion.. Requires textbook reading and relistening to lectures to gather the nuances.
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.
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.
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Learning Outcomes: By the end of this course, you will be able to
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