Suggested Text:
Grossman, D. and Frieder, O. Information Retrieval: Algorithms and Heuristics, Second Edition, Springer Publishers, ISBN 1-4020-3003-7 (hardcover), 1-4020-3004-5 (paperback), 2004.

Grading:

Class Participation	20%
Project	80%

Academic Integrity:
Each member of this course bears responsibility for maintaining the highest standards of academic integrity. All breaches of academic integrity must be reported immediately.

Late Assignment Policy:
Assignments must be submitted on or before their due date. No late assignments will be assigned a grade.

Class Participation:
Students must actively participate in class as this is a significant portion of the grade.

Handouts:

• Database introduction
• Information Retrieval introduction
• Data Warehouse introduction
• Data Mining introduction

Project:
Students will be required to write a short (approx. 5 page) paper summarizing the field of Information Systems. The paper should be well written in the student's own words. Although poorly written papers (poor grammar, punctuation, etc.) will certainly lose points, this is much preferable to papers which simply copy material from journals, reviews, online resources, or any other such material. In short, do not plagiarize material. Your paper will be automatically assigned a zero if discovered to have material taken from other sources and not assigned due credit.

All quoted or paraphrased elements in the paper must clearly be linked to the appropriate reference material; if you are not clear on how this should be done refer to any well written research paper or contact the TA for help. Papers must be turned in electronically via CourseInfo in Microsoft Word format, with all graphics (if necessary) embedded.

The paper should contain at least twenty relevant links to relevant web sites on the material. The due date for the paper is July 11th.

Schedule:

Date	Topics for Discussion
5/20	Database Systems: Practical Topic Overview
5/21	Text Database Processing: Practical Techniques, Integrating Structured Data and Text
5/27	Data Warehousing: Introduction
5/28	Practical Data Mining: Overview