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: | 加州大学伯克利分校 |
| 开课时间: | 2016-11-28 |
| 课程语种: | 英语 |
| 中文简介: |  描述 科学和技术数据集的大小和范围的迅速增长，导致人们需要一种将推理科学和计算科学融为一体的新颖的数据分析基础观点。从这些领域的经典观点不足以解决“大数据”中出现的问题，这从它们在计算机科学的初级水平上的明显分歧性质就可以明显看出，数据点数量的增长是必须“复杂”的源头可以通过算法或硬件加以驯服，而在统计中，数据点数量的增长是“简单性”的来源，因为推断通常更强，并且可以调用渐近结果。在形式上，由于核心统计理论中缺少诸如“运行时”之类的计算概念所发挥的作用以及核心计算理论中诸如“风险”之类的统计概念所缺乏的作用，这一差距显而易见。我提出了几种旨在将计算和统计联系起来的研究方法，包括隐私和通信约束下的推理问题，以及权衡推理速度和准确性的方法。 |
| 课程简介: | Description 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: | 计算思维; 推理思维; 数据科学 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-09-27：zkj |
| 最后编审: | 2020-10-22：chenxin |
| 阅读次数: | 40 |