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

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: Thur 10:00am - 11:00am  Course web site: http://www.cs.sjtu.edu.cn/~liwujun/course/wsm.html http://www.cs.sjtu.edu.cn/~liwujun/course/wsm.html  Teaching Assistant: TBD  Lecture Time: Wed 10:00 - 10:45 & 10:55 - 11:40 Fri 12:55 - 13:40 & 14:00 - 14:45  Lecture Venue: Rm 308, Rui-Qiu Chen Building( 陈瑞球楼 308) 2

3. Web Search and Mining Course Overview 3 Textbook  Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. Introduction to Information Retrieval. Cambridge University Press, 2008.  The English reprint edition ( 英文影印版 ) can be bought through China-Pub (http://www.china-pub.com/193197). You can also download it from the book website (http://nlp.stanford.edu/IR-book/information-retrieval- book.html).China-Pubbook website

4. Web Search and Mining Course Overview 4 Reference Books  Bruce Croft, Donald Metzler, and Trevor Strohman. Search Engines: Information Retrieval in Practice. Addison Wesley, 2009. (The English reprint edition can be bought through China-Pub.)China-Pub  Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, 2006.  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  Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Second Edition,2009. (http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html)http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html  Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.

5. Web Search and Mining Course Overview 5 Course Topics  Architecture of search engines  The basics of information retrieval (IR)  index construction and compression; Boolean retrieval; vector space model; evaluation of IR systems; relevance feedback and query expansion  Probabilistic IR and language models  Data mining and machine learning (ML) basics  supervised learning; unsupervised learning; matrix factorization  Graph mining, social search and recommender systems

6. Web Search and Mining Course Overview 6 Prerequisites  Data structure  Design and analysis of algorithms  Linear algebra  Probability theory

7. Web Search and Mining Course Overview 7 Grading Scheme  In class quizzes (30%)  Homework (30%)  Project + presentation (40%)

8. Web Search and Mining Course Overview 8 Late Assignments  Assignments turned in late will be penalized 20% per late day

9. Web Search and Mining 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. Web Search and Mining Course Overview 10 Question?