CS084《数据科学导论》
—2017暑期，康奈尔大学图灵奖得主John E. Hopcroft 教授主讲

授课人：霍普克罗夫特教授是美国康奈尔大学著名计算机科学教授，1985年图灵奖得主。他是美国科学院、工程院院士，是美国艺术与科学研究院院士。他曾先后在普林斯顿大学、康奈尔大学、斯坦福大学等著名高等学府工作，曾任职于一些著名科学研究机构如美国科学基金会和美国国家研究院。1992年到1998年5月，他被布什总统指定为监督国家科学基金会的国家科学委员会成员。2005年，John获得IEEE哈里·古德（Harry Goode）纪念奖，并且于2007年获得计算机研究协会的杰出贡献奖。

2011年起Hopcroft教授开始受邀为上海交通大学学生授课。6年间对上海交大人才培养工作和师资队伍建设工作倾注了大量精力。由于他对中国教育和科研的突出贡献，2016年国庆前夕，John 被授予“中国政府友谊奖”，由国务院总理李克强、副总理张高丽和马凯等领导颁奖并为中国科研及教育献计献策。2017年夏季学期面向上海交通大学全校学生授课。

课程简介：本课程将介绍现代数据科学，特别是机器学习的基础知识。未来信息时代的热点将是如何利用计算机从海量数据中去理解并提取有用数据。课程旨在为学生面向未来四十年计算机领域的应用和研究做准备。课程重点将从传统离散数学转向概率、统计以及数值方法等技术。

CS084: Fundamentals in Data Science
Summer 2017, by Prof. John E. Hopcroft

Scholars: Prof. John E. Hopcroft was honored with the A. M. Turing Award in 1986. He is a member of the National Academy of Sciences (NAS), the National Academy of Engineering (NAE) and a fellow of the American Academy of Arts and Sciences (AAAS), the American Association for the Advancement of Science, the Institute of Electrical and Electronics Engineers (IEEE), and the Association of Computing Machinery (ACM). In 1992, he was appointed by President Bush to the National Science Board (NSB), which oversees the National Science Foundation (NSF), and served through May 1998. From 1995-98, Hopcroft served on the National Research Council's Commission on Physical Sciences, Mathematics, and Applications.

In addition to these appointments, Hopcroft serves as a member of the Scientific Advisory Committee for the David and Lucile Packard Fellowships in Science and Engineering, the SIAM financial management committee, IIIT New Delhi advisory board, Microsoft's technical advisory board for research Asia, the Engineering Advisory Board, Seattle University, and the program committee for Chile Millennium Science Initiative. In late 2016, John was honored with the ‘Chinese government friendship award’ for his great contribution in improving the education and academia in China.

Description: While traditional areas of computer science remain highly important, increasingly researchers of the future will be involved with using computers to understand and extract usable information from massive data arising in applications, not just how to make computers useful on specific well-defined problems. This course intends to cover the theory likely to be useful in the next 40 years, and related topics gave students an advantage in the last 40 years. One of the major changes is the switch from discrete mathematics to more of an emphasis on probability, statistics, and numerical methods.