图 书 馆
概率图模型Probabilistic Graphical Models |
|
| 课程网址: | http://videolectures.net/mlss05au_roweis_pgm/ |
| 主讲教师: | Sam Roweis |
| 开课单位: | 无 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  我的讲座将涵盖图形模型的基础，也称为贝叶斯（Belief）网络（工作）。我们将介绍在机器学习中使用概率表示和推理不确定知识的基本动机，并介绍图形模型作为大型联合概率分布的定性和定量规范。我们将看到在此框架中可以转换多少个常见的分类，回归和聚类模型。我们将介绍用于图形模型结构推理的基本算法（称为置信传播）。我们还将介绍从数据中学习模型的主要方法（参数估计）。该课程将侧重于定向模型和基本算法，但时间和学生的意愿允许，我还将尝试对无向模型，近似推理和学习，结构发现和当前应用进行一些初步的解释。 |
| 课程简介: | My lectures will cover the basics of graphical models, also known as Bayes(ian) (Belief) Net(work)s. We will cover the basic motivations for using probabilities to represent and reason about uncertain knowledge in machine learning, and introduce graphical models as a qualitative and quantitative specification of large joint probability distributions. We will see how many common classification, regression and clustering models can be cast in this framework. We will cover the basic algorithm (called belief propagation) for inference in graphical model structures. We will also cover the major approaches to learning models from data (parameter estimation). The course will focus on directed models and the basic algorithms, but time and student desire permitting, I will also try to give some preliminary explanations of undirected models, approximate inference and learning, structure discovery and current applications. |
| 关 键 词: | 概率图; 叶贝斯网络; 回归模型; 聚类模型; 定向模型; 无向模型; 近拟推理 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-04-01：zhouxj |
| 最后编审: | 2020-05-25：cxin |
| 阅读次数: | 63 |