图 书 馆
大型信息图的线性代数与机器学习Linear Algebra and Machine Learning of Large Informatics Graphs |
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| 课程网址: | http://videolectures.net/nipsworkshops2010_mahoney_lam/ |
| 主讲教师: | Michael Mahoney |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 2011-01-13 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Very large informatics graphs such as large social and information networks typically have properties that render many popular machine learning and data analysis tools largely inappropriate. While this is problematic for these applications, it also suggests that these graphs may be useful as a test case for the development of new algorithmic tools that may then be applicable much more generally. Many of the popular machine learning and data analysis tools rely on linear algebra, and they are typically used by calling traditional numerical linear algebra code as a black box. After briefly reviewing some of the structural properties of large social and information networks that are responsible for the inapplicability of traditional linear algebra and machine learning tools, I will describe several examples of "new linear algebra" and "new machine learning" that arise from the analysis of such informatics graphs. These new directions involve looking "inside" the black box, and they place very different demands on the linear algebra than are traditionally placed by numerical, scientific computing, and small-scale machine learning applications. |
| 关 键 词: | 计算机科学; 机器学习; 线性代数 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-05-30：张荧(课程编辑志愿者) |
| 阅读次数: | 56 |