图 书 馆
概率图模型Probabilistic Graphical Models |
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| 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: |  |
| 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: | 贝叶斯; 概率分布; 回归和聚类模型; 无向模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-11：liush |
| 阅读次数: | 41 |