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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/probabilistic-graphical-models
课程评论：没有评论
课程名称：概率图模型 1：表示 课程概述：概率图模型（PGMs）是一个丰富的框架，用于编码复杂领域中的概率分布：多个相互作用的随机变量的联合（多变量）分布。这些表示位于统计学和计算机科学的交汇处，依赖于概率论、图算法、机器学习等概念。它们是许多应用中最先进方法的基础，如医学诊断、图像理解、语音识别、自然语言处理等。同时，它们也是构建许多机器学习问题的基础工具。本课程是三个模块中的第一个，介绍了两种基本的PGM表示法：依赖有向图的贝叶斯网络和使用无向图的马尔可夫网络。课程讨论了这些表示的理论性质及其在实际中的应用（推荐参加荣誉轨道以进行实际操作）。 课程大纲： 1. **贝叶斯网络（有向模型）** 该模块定义了贝叶斯网络的表示及其语义，分析了图结构与通过该图表示的分布独立性属性之间的关系，并提供如何将现实世界情况建模为贝叶斯网络的实用建议。 2. **贝叶斯网络的结构化条件概率分布（CPDs）** 由于贝叶斯网络中条件概率分布的表格表示在父节点数量上呈指数增长，因此存在多种其他形式的CPD，利用依赖模型中的某种结构实现更紧凑的表示。本模块描述了实际中最常用的一些结构。 3. **马尔可夫网络（无向模型）** 本模块描述基于无向图表示的马尔可夫网络（也称为马尔可夫随机场）：我们讨论这些模型的表示及其语义，分析这些图编码的分布独立性属性及其与图结构的关系，并比较它们与贝叶斯网络编码的独立性，以提供适用于特定场景的模型选择见解。 4. **决策制定** 在这个模块中，我们讨论不确定条件下的决策制定任务，描述决策理论框架，包括效用函数的一些方面。接着，我们讲解如何将决策情境编码为图形模型——影响图，并进一步探讨这些模型如何提供对决策和信息收集价值的洞察。 5. **知识工程与总结** 本模块概述了图形模型表示及在将情景建模为图形模型时一些现实世界的考虑因素，同时包括课程的期末考试。 通过这门课程，学生将能深入了解概率图模型及其应用，掌握构建和分析图模型所需的基础知识。
Part: 1
Title:Bayesian Network (Directed Models)
Description:In this module, we define the Bayesian network representation and its semantics. We also analyze the relationship between the graph structure and the independence properties of a distribution represented over that graph. Finally, we give some practical tips on how to model a real-world situation as a Bayesian network.
Part: 2
Title:Structured CPDs for Bayesian Networks
Description:A table-based representation of a CPD in a Bayesian network has a size that grows exponentially in the number of parents. There are a variety of other form of CPD that exploit some type of structure in the dependency model to allow for a much more compact representation. Here we describe a number of the ones most commonly used in practice.
Part: 3
Title:Markov Networks (Undirected Models)
Description:In this module, we describe Markov networks (also called Markov random fields): probabilistic graphical models based on an undirected graph representation. We discuss the representation of these models and their semantics. We also analyze the independence properties of distributions encoded by these graphs, and their relationship to the graph structure. We compare these independencies to those encoded by a Bayesian network, giving us some insight on which type of model is more suitable for which scenarios.
Part: 4
Title:Decision Making
Description:In this module, we discuss the task of decision making under uncertainty. We describe the framework of decision theory, including some aspects of utility functions. We then talk about how decision making scenarios can be encoded as a graphical model called an Influence Diagram, and how such models provide insight both into decision making and the value of information gathering.
Part: 5
Title:Knowledge Engineering & Summary
Description:This module provides an overview of graphical model representations and some of the real-world considerations when modeling a scenario as a graphical model. It also includes the course final exam.
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 first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.
