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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/probabilistic-graphical-models-3-learning
课程评论：没有评论
课程名称: 概率图模型 3：学习 概述: 概率图模型 (PGMs) 是一种丰富的框架，用于在复杂领域中编码概率分布：多元分布涵盖大量相互作用的随机变量。这些表示方法位于统计学与计算机科学的交叉点，依赖于概率论、图形算法、机器学习等概念。PGMs 是多种应用领域中最先进方法的基础，包括医学诊断、图像理解、语音识别、自然语言处理等，还是许多机器学习问题的基础工具。 本课程是三门课程中的第三门。在第一门课程中，重点是表示方法；在第二门课程中，重点是推理；本课程关注学习的问题：如何从示例数据集中学习 PGM。课程讨论了有向和无向模型中的参数估计的关键问题，以及有向模型的结构学习任务。荣誉追踪部分包含两个实践编程作业，学员需实现和应用两个常用学习算法的关键程序到实际问题中。 课程大纲: - 第一部分: 贝叶斯网络中的参数估计 - 讨论概率图模型中最基本的学习问题：贝叶斯网络中的参数估计，探讨最大似然估计及其问题，然后讨论贝叶斯估计如何改善这些问题。 - 第二部分: 学习无向模型 - 讨论马尔可夫网络（无向图模型）的参数估计问题，这一任务在概念和计算上都比贝叶斯网络中的参数估计更为复杂。 - 第三部分: 学习贝叶斯网络结构 - 讨论学习贝叶斯网络结构的问题，探讨如何将这一问题表述为图结构空间上的优化问题，以及如何评分不同结构以权衡与数据的适应性和模型复杂性。 - 第四部分: 学习包含缺失数据的贝叶斯网络 - 讨论在某些数据案例中部分变量未被完全观测到的情况下学习模型的问题，以及为何这种情况比完全可观测的情况更为复杂，介绍广泛应用的期望最大化 (EM) 算法。 - 第五部分: 概率图模型总结 - 提供 PGM 方法的整体概述，讨论在实践中使用该框架时的一些现实取舍，涉及前三门 PGM 课程的主题。
Part: 1
Title:Parameter Estimation in Bayesian Networks
Description: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.
Part: 2
Title:Learning Undirected Models
Description: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.
Part: 3
Title:Learning BN Structure
Description: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.
Part: 4
Title:Learning BNs with Incomplete Data
Description: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.
Part: 5
Title:PGM Wrapup
Description:This module contains an overview of PGM methods as a whole, discussing some of the real-world tradeoffs when using this framework in practice. It refers to topics from all three of the PGM courses.
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. This course is the third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem.
