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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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
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
100% 在線
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第 3 門課程（共 3 門）
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高級
完成時間大約為24 小時
英語（English）
字幕：英語（English）
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
4.6
37 條評論來自Probabilistic Graphical Models 3: Learning的熱門評論
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.
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.
關於 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.
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
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
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