Web Search and Mining Course Overview Web Search
Web Search and Mining Course Overview Web Search and Mining Wu-Jun Li Department of Computer Science and Engineering Shanghai Jiao Tong University Lecture 0: Course Overview 1
Web Search and Mining Course Overview General Information § Instructor: Wu-Jun Li (李武军) § § Email: liwujun@cs. sjtu. edu. cn Homepage: http: //www. cs. sjtu. edu. cn/~liwujun Office: Rm 3 -537, SEIEE Building Office Hours: Thur 10: 00 am - 11: 00 am § Course web site: 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
Web Search and Mining Course Overview 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-retrievalbook. html). 3
Web Search and Mining Course Overview 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. ) § 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. ) § 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/Elem. Stat. Learn/index. html) § Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006. 4
Web Search and Mining Course Overview 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 5
Web Search and Mining Course Overview Prerequisites § Data structure § Design and analysis of algorithms § Linear algebra § Probability theory 6
Web Search and Mining Course Overview Grading Scheme § In class quizzes (30%) § Homework (30%) § Project + presentation (40%) 7
Web Search and Mining Course Overview Late Assignments § Assignments turned in late will be penalized 20% per late day 8
Web Search and Mining Course Overview 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. 9
Web Search and Mining Course Overview Question? 10
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