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
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Web Search and Mining
Course Overview
General Information
 Instructor: Wu-Jun Li (李武军)
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Email: liwujun@cs.sjtu.edu.cn
Homepage: http://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
 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)
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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).
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Web Search and Mining
Course Overview
Reference Books
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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.)
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Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents and Usage Data.
Springer, 2006.
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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.)
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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)
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Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
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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
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Web Search and Mining
Course Overview
Prerequisites
 Data structure
 Design and analysis of algorithms
 Linear algebra
 Probability theory
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Web Search and Mining
Course Overview
Grading Scheme
 In class quizzes (30%)
 Homework (30%)
 Project + presentation (40%)
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Web Search and Mining
Course Overview
Late Assignments
 Assignments turned in late will be penalized 20% per
late day
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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.
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Web Search and Mining
Course Overview
Question?
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