图 书 馆
从大量数据中学习从实践到理论From Practice to Theory in Learning from Massive Data |
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| 课程网址: | http://videolectures.net/kdd2016_elkan_massive_data/ |
| 主讲教师: | Charles Elkan |
| 开课单位: | 加州大学 |
| 开课时间: | 2016-10-12 |
| 课程语种: | 英语 |
| 中文简介: |  本讲座将讨论有关Amazon如何将机器学习应用于大规模数据的示例，并讨论受这些应用启发的开放研究问题。一个重要的问题是如何区分可能受到影响的用户和仅可能做出响应的用户。另一个问题是如何衡量和最大化电影和其他推荐的长期利益。第三个问题是如何在共享数据的同时可证明地保护用户的隐私。注意：演讲中的信息已经公开，表达的意见将完全是个人的。 |
| 课程简介: | This talk will discuss examples of how Amazon applies machine learning to large-scale data, and open research questions inspired by these applications. One important question is how to distinguish between users that can be influenced, versus those who are merely likely to respond. Another question is how to measure and maximize the long-term benefit of movie and other recommendations. A third question, is how to share data while provably protecting the privacy of users. Note: Information in the talk is already public, and opinions expressed will be strictly personal. |
| 关 键 词: | 机器学习; 共享数据; 数据隐私 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-05-28：liyy |
| 最后编审: | 2021-05-28：liyy |
| 阅读次数: | 39 |