课程信息
4.6
174 个评分
28 个审阅
专项课程

第 3 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24 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

AlgorithmsExpectation–Maximization (EM) AlgorithmGraphical ModelMarkov Random Field
专项课程

第 3 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24 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 16 分钟

Learning: Overview

This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course....
Reading
1 个视频 (总计 16 分钟)
Video1 个视频
完成时间(小时)
完成时间为 1 小时

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....
Reading
6 个视频 (总计 59 分钟)
Video6 个视频
Regularization: Cost Function 10分钟
Evaluating a Hypothesis 7分钟
Model Selection and Train Validation Test Sets 12分钟
Diagnosing Bias vs Variance 7分钟
Regularization and Bias Variance11分钟
完成时间(小时)
完成时间为 2 小时

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....
Reading
5 个视频 (总计 77 分钟), 2 个测验
Video5 个视频
Maximum Likelihood Estimation for Bayesian Networks15分钟
Bayesian Estimation15分钟
Bayesian Prediction13分钟
Bayesian Estimation for Bayesian Networks17分钟
Quiz2 个练习
Learning in Parametric Models18分钟
Bayesian Priors for BNs8分钟
2
完成时间(小时)
完成时间为 21 小时

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....
Reading
3 个视频 (总计 52 分钟), 2 个测验
Video3 个视频
Maximum Likelihood for Conditional Random Fields13分钟
MAP Estimation for MRFs and CRFs9分钟
Quiz1 个练习
Parameter Estimation in MNs6分钟
3
完成时间(小时)
完成时间为 17 小时

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....
Reading
7 个视频 (总计 106 分钟), 3 个测验
Video7 个视频
Likelihood Scores16分钟
BIC and Asymptotic Consistency11分钟
Bayesian Scores20分钟
Learning Tree Structured Networks12分钟
Learning General Graphs: Heuristic Search23分钟
Learning General Graphs: Search and Decomposability15分钟
Quiz2 个练习
Structure Scores10分钟
Tree Learning and Hill Climbing8分钟
4
完成时间(小时)
完成时间为 22 小时

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....
Reading
5 个视频 (总计 83 分钟), 3 个测验
Video5 个视频
Expectation Maximization - Intro16分钟
Analysis of EM Algorithm11分钟
EM in Practice11分钟
Latent Variables22分钟
Quiz2 个练习
Learning with Incomplete Data8分钟
Expectation Maximization14分钟
4.6
28 个审阅Chevron Right
工作福利

12%

通过此课程获得实实在在的工作福利
职业晋升

14%

加薪或升职

热门审阅

创建者 ZZFeb 14th 2017

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.

创建者 JRJan 29th 2018

Great course! It is pretty difficult - be prepared to study. Leave plenty of time before the final exam.

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

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  • 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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