Earlier and broader access to machine learning

更早、更广泛地接触机器学习

• 批准号: 1141033
• 负责人: Lillian Cassel
• 金额: $ 19.99万
• 依托单位: Villanova University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1141033&HistoricalAwards=false
• 关键词: Earlier; broader; access; machine; learning

The project goal is to develop a collection of modules to be used in various combinations, to introduce undergraduate students to the concepts and techniques of Machine Learning.The project brings the concepts and techniques of Machine Learning to undergraduates, and explores the feasibility of making modular presentations of this material in formats for use in various combinations to meet the needs in a variety of subject areas. The project motivates the study by working with real scenarios from the subject domains and by using real data sets provided by an advisory board.This project increases the pool of graduates available for research and development in areas where large quantities of data must be analyzed. The PIs attract students from underrepresented groups, both as testers of the materials and as students in the initial course deployments. The resulting modules are freely available for adaptation and reuse.

项目目标是开发一个用于各种组合的模块的集合，向本科生介绍机器学习的概念和技术。该项目带来了机器学习的概念和技巧，以使本科生的概念和技巧探索，并探讨了在各种组合中使用该材料的模块化表现的可行性，以满足各种主题的组合，以满足各种各样的主题。该项目通过使用顾问委员会提供的真实数据集并使用实际数据集来激发研究。该项目增加了必须在必须分析大量数据的领域中可用于研究和开发的毕业生库。 PI吸引了来自代表性不足的群体的学生，无论是作为材料的测试人员还是作为最初课程部署的学生。最终的模块可免费提供适应和重复使用。