皇冠体育app

Web Search and Mining Fall 2012

Announcements

Nov 20: Solution for Homework 3 posted.
Nov 02: Homework 3 posted. Due on Nov 09.
Nov 01: Solution for Homework 2 posted.
Oct 24: Homework 2 posted. Due on Oct 31.
Oct 16: Project posted.
Oct 16: Solution for Homework 1 posted.
Sep 27: Homework 1 posted. Due on Oct 10.
Sep 11: Course website launched.

 

Instructor

Wu-Jun Li ([email protected]; //www.cs.sjtu.edu.cn/~liwujun; Rm 3-537, SEIEE Building; 34206661)
Office Hours: Thur 10:00am - 11:00am

 

Teaching Assistant

Zhi-Qin Yu ([email protected])

 

Lecture Time and Venue

Wed 10:00 - 10:45 & 10:55 - 11:40
Fri 12:55 - 13:40 & 14:00 - 14:45
Rm 308, Rui-Qiu Chen Building(陈瑞球楼308)

 

Textbook

[IIR]: 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. You can also download it from the book website.

 

Reference Books

[SE]: 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.)

[WDM]: Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, 2006.

[DM]: 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.)

[ESL]: Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Second Edition, 2009.
(//www-stat.stanford.edu/~tibs/ElemStatLearn/index.html)

[PRML]: Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.

Course Topics and Schedule (tentative)

(I acknowledge Christopher D. Manning for allowing me to use his slides, and to make some modifications if desired. The slides for Lec 1 are mainly adapted from those provided by Bruce Croft.)

Date	Topics	Slides	Readings
Sep 12	Introduction: Web search overview, Web crawling and indexes	Lec 0 Lec 1	IIR Ch. 19 - 20 SE Ch. 1 - 3 Web Crawling (from Bing Liu)
Sep 14	Boolean retrieval	Lec 2	IIR Ch.1
Sep 19	The term vocabulary and postings lists	Lec 3	IIR Ch.2
Sep 21	Dictionaries and tolerant retrieval	Lec 4	IIR Ch.3
Sep 26	Index construction and compression	Lec 5 Lec 6	IIR Ch.4 - 5
Sep 28	Scoring, term weighting, and the vector space model	Lec 7	IIR Ch.6
Oct 10	Computing scores in a complete search system	Lec 8	IIR Ch.7
Oct 12	Evaluation and relevance feedback	Lec 9 Lec 10	IIR Ch.8 - 9
Oct 17	Probabilistic information retrieval	Lec 11	IIR Ch.11
Oct 19	Language models	Lec 12	IIR Ch.12
Oct 21	Form groups,and select a paper (a topic). Then send the group and paper information to TA. Deadline: 23:59pm
Oct 24	Matrix factorization and latent semantic indexing	Lec 13	IIR Ch.18
Oct 26	Link analysis: PageRank and HITS	Lec 14	IIR Ch.21
Oct 31	Supervised learning: classification	Lec 15	IIR Ch.13 - 15
Nov 02	Unsupervised learning: clustering	Lec 16	IIR Ch. 16 -17

 

 

Prerequisites

data structure, design and analysis of algorithms, linear algebra, probability theory

 

Grading Scheme

1. In class quizzes (30%)

2. Homework (30%)

Homework 1
Homework 2
Homework 3

3. Project + presentation (40%)

Project

 

Late Assignments

Assignments turned in late will be penalized 20% per late day.

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.