CRCD: Machine Learning: A Multidisciplinary Computer Engineering Graduate Program

CRCD：机器学习：多学科计算机工程研究生课程

• 批准号: 9980296
• 负责人: Sunanda Mitra
• 金额: $ 49.38万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-15 至 2003-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9980296&HistoricalAwards=false
• 关键词: CRCD; Machine; Learning; Multidisciplinary; Computer

EIA 9980296Wunsch, Donald Dunyak, James P.Feldkamp, Lee A.Texas Tech UniversityCRCD: Machine Learning: A Multi-disciplinary Computer Engineering Graduate ProgramThis CISE Combined Research-Curriculum Development proposal requests funds to develop a machine learning track in Computer Engineering M.S. and Ph.D. programs. The machine learning track will be interdisciplinary and will include three new courses at the senior undergraduate/graduate level: Advanced Neural Networks (CS), Dynamic Programming (Math), and Adaptive Critic Designs (EE). The new courses will include interaction with leading industrial researchers through distance learning. In addition to the new courses, there will be four laboratory rotations: Computational Intelligence Laboratory (EE), Industrial Applications Laboratory (EE), Applied Probability Laboratory (Math), and Software Engineering Laboratory (CS). For the laboratory rotation courses, the students will be organized in teams and directed towards real research problems with direct industrial relevance. As a capstone experience, students will participate in a one-semester internship at a national laboratory or industrial facility. The PI's will concurrently be developing a textbook on Adaptive Critic Designs. This project has the potential to have a significant impact. It could serve as a model for the design of new interdisciplinary degree programs at other institutions.

EIA 9980296 Wunsch，Donald Dunyak，James P.Feldkamp，Lee A.德克萨斯理工大学CRCD：机器学习：多学科计算机工程研究生课程这份联合研究-课程开发提案要求资金在计算机工程硕士和博士课程中开发机器学习轨道。机器学习将是跨学科的，将包括三门高级本科生/研究生水平的新课程：高级神经网络(CS)、动态编程(Math)和自适应批评设计(EE)。新课程将包括通过远程学习与领先的工业研究人员互动。除了新课程外，还将有四个实验室轮换：计算智能实验室(EE)、工业应用实验室(EE)、应用概率实验室(Math)和软件工程实验室(CS)。对于实验室轮换课程，学生将被组织成小组，并针对与工业直接相关的实际研究问题。作为一次巅峰体验，学生们将在国家实验室或工业设施参加为期一学期的实习。PI将同时开发一本关于适应性批评设计的教科书。这个项目有可能产生重大影响。它可以作为其他机构设计新的跨学科学位课程的典范。