1. Mining Massive Datasets Course Overview 1 Wu-Jun Li Department of Computer Science and Engineering Shanghai Jiao Tong University Lecture 0: Course Overview Mining Massive Datasets

2. Course Overview 2 General Information  Instructor: Wu-Jun Li ( 李武军）  Email: liwujun@cs.sjtu.edu.cnliwujun@cs.sjtu.edu.cn  Homepage: http://www.cs.sjtu.edu.cn/~liwujunhttp://www.cs.sjtu.edu.cn/~liwujun  Office: Rm 3-537, SEIEE Building  Office Hours: Tue 14:00 - 15:00  Course web site: http://www.cs.sjtu.edu.cn/~liwujun/course/mmds.html http://www.cs.sjtu.edu.cn/~liwujun/course/mmds.html  Teaching Assistant: Zhi-Qin Yu （余志琴）  Email: xiaoyu199175@gmail.com  Office Hours: TBD; Rm 3-503, SEIEE Building  Time and Venue: Mon 14:00 – 15:40; Wed 10:00 - 11:40; Fri 08:00 - 09:40 ; Rm 105, Dong Shang Yuan ( 东上院 105) 2

3. Mining Massive Datasets Course Overview 3 Textbook  Anand Rajaraman and Jeffrey D. Ullman. Mining of Massive Datasets. Cambridge University Press, 2011. You can download it from the book website (http://i.stanford.edu/~ullman/mmds.html).book website

4. Mining Massive Datasets Course Overview 4 Reference Books  Jiawei Han, and Micheline Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, Second Edition, 2006. (The English reprint edition can be bought through China-Pub.) China-Pub  Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.  Chuck Lam. Hadoop in Action. Manning Publications, First Edition, 2010.  周憬宇，李武军，过敏意. 《飞天开放平台编程指 南 - 阿里云计算的实践》. 电子工业出版社， 2013 年 3 月.

5. Mining Massive Datasets Course Overview 5 Course Topics  Data-Intensive Scalable Computing (DISC)  Cloud Computing  MapReduce and Hadoop  Data Mining and Machine Learning  Basics: supervised learning; unsupervised learning; matrix factorization  Large-scale (distributed) implementations with Hadoop  Data-Intensive Applications  Search, link analysis, recommender systems, mining data streams, advertising on Web

6. Mining Massive Datasets Course Overview 6 Prerequisites  Data structure  Design and analysis of algorithms  Linear algebra  Probability theory  Programming languages : Java, c++

7. Mining Massive Datasets Course Overview 7 Grading Scheme  Class attendance (10%)  Homework (20%)  Exam (40%): Final (40%)  Project (30%)  3 students / group

8. Mining Massive Datasets Course Overview 8 Late Assignments  Assignments turned in late will be penalized 20% per late day

9. Mining Massive Datasets Course Overview 9 Academic Honor Code  Honesty and integrity are central to the academic work.  All your submitted assignments must be entirely your own (or your own group's).  Any student found cheating or performing plagiarism will receive a final score of zero for this course.

10. Mining Massive Datasets Course Overview 10 Questions?