图 书 馆
支持人们和社会的数据、预测和决策Data, Predictions, and Decisions in Support of People and Society |
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| 课程网址: | http://videolectures.net/kdd2014_horvitz_people_society/ |
| 主讲教师: | Eric Horvitz |
| 开课单位: | 微软公司 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  深层的社会效益将来自数据可用性和计算程序的进步，这些经验可用于从大型数据集中挖掘见解和推论。我将描述利用数据进行预测和指导决策，接触运输，医疗保健，在线服务和交互式系统中的工作的工作。我将开始努力学习并提出可预测大城市地区交通流量的预测模型。从地面到空中，我将讨论飞机的融合数据以推断大气状况，并利用这些结果来增强航空运输。然后，我将重点介绍建立和部署临床医学预测模型的经验。我将展示有关结果和干预措施的推论如何提供见解并指导决策。除了医院捕获的数据之外，我还将讨论将将从Web服务中获取的匿名行为数据转换为大规模传感器网络以实现公共卫生的承诺，包括努力确定药物的不良作用并了解人群的疾病。最后，我将通过描述如何使用机器学习来利用人与机器知识的互补性来解决科学和社会中具有挑战性的问题。 |
| 课程简介: | Deep societal benefits will spring from advances in data availability and in computational procedures for mining insights and inferences from large data sets. I will describe efforts to harness data for making predictions and guiding decisions, touching on work in transportation, healthcare, online services, and interactive systems. I will start with efforts to learn and field predictive models that forecast flows of traffic in greater city regions. Moving from the ground to the air, I will discuss fusing data from aircraft to make inferences about atmospheric conditions and using these results to enhance air transport. I will then focus on experiences with building and fielding predictive models in clinical medicine. I will show how inferences about outcomes and interventions can provide insights and guide decision making. Moving beyond data captured by hospitals, I will discuss the promise of transforming anonymized behavioral data drawn from web services into large-scale sensor networks for public health, including efforts to identify adverse effects of medications and to understand illness in populations. I will conclude by describing how we can use machine learning to leverage the complementarity of human and machine intellect to solve challenging problems in science and society. |
| 关 键 词: | 指导决策; Web服务; 预测模型 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-01：zyk |
| 最后编审: | 2020-11-04：chenxin |
| 阅读次数: | 38 |