中科院计算所裴建教授免费讲授“大数据分析和数据挖掘”课程
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Big Data Analytics and Data Mining
| Course Name | Big Data Analytics and Data Mining(大数据分析和数据挖掘) |
| Instructor | Jian Pei, Simon Fraser University http://www.cs.sfu.ca/~jpei/ |
| Address | Institute of Computing Technology,Chinese Academy of Sciences No.6 Kexueyuan South Road Zhongguancun,Haidian District Beijing,China 北京海淀区中关村科学院南路6号中科院计算所(地图) |
| Time | May 5th~9th, 2014 |
| Contact |
课程免费，食宿自理；报名请附上姓名、学校和研究方向等信息；为保证授课效果，限额50~60人。 报名截止日期为4月10日，报名结果将于4月15日在本页面公布。 |
Introduction
This course provides a quick introduction to the exciting frontiers of big data analytics and data mining. The focus is on the essential concepts and techniques, the fundamental principles, as well as some active research topics. The audience is assumed to be solid in the popular discrete mathematics (including basics in set theory, abstract algebra, logics, and graph theory), algorithm analysis and design, and basic probability and statistics. C++ or Java programming is expected. Basic understanding of data mining, such as a quick scan of the textbook, would be helpful, though the course itself is self-contained and the basic concepts will be reviewed before the advanced topics are discussed.
The format of the course is a combination of lectures and classroom discussion. It is a 5 day course, 3 hours every morning and 2 hours every afternoon. In general, lectures on basic concepts and principles will be presented in the mornings, while advanced topics and research directions will be discussed in the afternoons. The tentative schedule is as follows, which is subject to change without notice. The course components may also be customized according to audiences interest.
Schedule
| May 5th | Morning – Introduction (big data, data mining, data analytics, and applications) – Cloud computing Afternoon – MapReduce basics |
| May 6th | Morning – Multidimensional data analysis, data warehousing, business intelligence Afternoon – Advanced topic: multidimensional analysis on complex data and big data |
| May 7th | Morning – Frequent pattern mining Afternoon – Advanced topic: advanced frequent pattern mining and applications |
| May 8th | Morning – Classification Afternoon – Advanced topic: advanced classification methods, predictive analytics |
| May 9th | Morning – Clustering analysis Afternoon – Advanced topics: Case study: data mining in healthcare informatics, crowdsourcing |
About the instructor
Jian Pei is a professor at the School of Computing Science at Simon Fraser University, Canada. He received a Ph.D. degree in Computing Science from the same school in 2002, under Dr. Jiawei Han’s supervision. His research interests can be summarized as developing effective and efficient data analysis techniques for novel data intensive applications. Particularly, he is currently interested in various techniques of data mining, information retrieval, data warehousing, online analytical processing, and database systems, as well as their applications in social networks, network security informatics, healthcare informatics, business intelligence, and web search. His research outcome has been adopted by industry production systems. He has published prolifically in premier academic venues. His publications have been cited more than 30,000 times. His research has been supported in part by many government agencies and many industry partners. Currently, his priority in research is on developing industry relations and collaboration, and transferring his technologies to industry applications. He is also actively serving the professional communities. He is current the editor-in-chief of IEEE Transactions of Knowledge and Data Engineering, and an associate editor or editorial board member of several premier journals in his areas. He has played key roles in many top academic conferences. He is a director of ACM SIGKDD and an ACM Distinguished Speaker. He received several prestigious awards. He is a fellow of IEEE and a senior member of ACM.
For more information about dragonstar program, please visit http://dragonstar.ict.ac.cn/dragonstar/index.asp
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