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信息几何Information Geometry |
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| 课程网址: | http://videolectures.net/mlss05us_dasgupta_ig/ |
| 主讲教师: | Sanjoy Dasgupta |
| 开课单位: | 加州大学圣地亚哥分校 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This tutorial will focus on entropy, exponential families, and information projection. We'll start by seeing the sense in which entropy is the only reasonable definition of randomness. We will then use entropy to motivate exponential families of distributions — which include the ubiquitous Gaussian, Poisson, and Binomial distributions, but also very general graphical models. The task of fitting such a distribution to data is a convex optimization problem with a geometric interpretation as an "information projection": the projection of a prior distribution onto a linear subspace (defined by the data) so as to minimize a particular information-theoretic distance measure. This projection operation, which is more familiar in other guises, is a core optimization task in machine learning and statistics. We'll study the geometry of this problem and discuss two popular iterative algorithms for it. |
| 关 键 词: | 统计; 数学; 信息几何 |
| 课程来源: | 视频讲座网公开课 |
| 最后编审: | 2020-06-01：王勇彬(课程编辑志愿者) |
| 阅读次数: | 327 |