图 书 馆
论计算思维、推理思维与数据科学On Computational Thinking, Inferential Thinking and Data Science |
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| 课程网址: | http://videolectures.net/BIDSAconference2016_jordan_computational... |
| 主讲教师: | Michael I. Jordan |
| 开课单位: | 加州大学 |
| 开课时间: | 2016-11-28 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in "Big Data" is apparent from their sharply divergent nature at an elementary level---in computer science, the growth of the number of data points is a source of "complexity" that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of "simplicity" in that inferences are generally stronger and asymptotic results can be invoked. On a formal level, the gap is made evident by the lack of a role for computational concepts such as "runtime" in core statistical theory and the lack of a role for statistical concepts such as "risk" in core computational theory. I present several research vignettes aimed at bridging computation and statistics, including the problem of inference under privacy and communication constraints, and methods for trading off the speed and accuracy of inference. |
| 关 键 词: | 计算; 统计; 数学思维 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-09：yxd |
| 最后编审: | 2020-11-09：zyk |
| 阅读次数: | 41 |