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所在平台: Coursera专项课程 课程类别: 计算机科学 大学或机构: CourseraNew |
课程主页: https://www.coursera.org/specializations/probabilistic-graphical-models
课程评论：没有评论
课程名称：概率图形模型专业化 概述：概率图形模型（PGM）是一个用于在复杂领域中编码概率分布的丰富框架。这些模型能够处理大量相互交互的随机变量的联合（多变量）分布。PGM结合了概率论、图算法、机器学习等多种概念，位于统计学与计算机科学的交叉点。它们是医学诊断、图像理解、语音识别、自然语言处理等多种应用中最先进方法的基础，同时也是许多机器学习问题的基础工具。 课程大纲： 1. **概率图形模型 1：表示** - 描述：由斯坦福大学提供，此课程主要介绍概率图形模型的表示方法。 - 课程链接：[课程 1](https://www.coursera.org/learn/probabilistic-graphical-models) 2. **概率图形模型 2：推断** - 描述：斯坦福大学提供的课程，重点讲解在概率图形模型中进行推断的技术。 - 课程链接：[课程 2](https://www.coursera.org/learn/probabilistic-graphical-models-2-inference) 3. **概率图形模型 3：学习** - 描述：同样由斯坦福大学提供，学习如何在概率图形模型中进行学习。 - 课程链接：[课程 3](https://www.coursera.org/learn/probabilistic-graphical-models-3-learning) 本专业化课程适合对概率建模及其应用感兴趣的学习者，帮助他们深入理解PGM的基本概念和应用。
Course Link: https://www.coursera.org/learn/probabilistic-graphical-models
Name:Probabilistic Graphical Models 1: Representation
Description:Offered by Stanford University. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over ... Enroll for free.
Course Link: https://www.coursera.org/learn/probabilistic-graphical-models-2-inference
Name:Probabilistic Graphical Models 2: Inference
Description:Offered by Stanford University. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over ... Enroll for free.
Course Link: https://www.coursera.org/learn/probabilistic-graphical-models-3-learning
Name:Probabilistic Graphical Models 3: Learning
Description:Offered by Stanford University. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over ... Enroll for free.
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.
概率图形模型专业化：概率图形模型（PGM）是一个丰富的框架，用于编码复杂域上的概率分布：大量相互交互的随机变量上的联合（多变量）分布。这些表示法依赖于概率论，图算法，机器学习等概念，位于统计学与计算机科学的交叉点上。它们是医学诊断，图像理解，语音识别，自然语言处理以及许多其他应用程序中最先进方法的基础。它们还是解决许多机器学习问题的基础工具。
