WPI Worcester Polytechnic Institute

Computer Science Department
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CS 444X Data Mining and Knowledge Discovery in Databases 
SYLLABUS - D Term 2003

PROF. CAROLINA RUIZ 

WARNING: Small changes to this syllabus may be made during the course of the term. 
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COURSE DESCRIPTION:

This undergraduate course provides an introduction to Knowledge Discovery in Databases (KDD) and Data Mining. KDD deals with data integration techniques and with the discovery, interpretation and visualization of patterns in large collections of data. Topics covered in this course include data warehousing and mediation techniques; data mining methods such as rule-based learning, decision trees, association rules and sequence mining; and data visualization. The work discussed originates in the fields of artificial intelligence, machine learning, statistical data analysis, data visualization, databases, and information retrieval. Several scientific and industrial applications of KDD will be described. In particular, current applications to bioinformatics, e-commerce, and web mining will be studied.

RECOMMENDED BACKGROUND:

CS4341 Introduction to Artificial Intelligence, MA2611 Applied Statistics I, and CS3431 Database Systems I.


CLASS MEETING:

Mondays, Tuesdays, Thursdays, Fridays 1:00-1:50 pm
ROOM: FL311
Please come to class on time and stay for the whole class period.

Students are also encouraged to attend the Knowledge Discovery in Databases and Data Mining Research Group (KDDRG) Seminar Fridays at 2 pm in Beckett Conference Room (FL246).


PROFESSOR:

Prof. Carolina Ruiz
ruiz@cs.wpi.edu
Office: FL 232
Phone Number: (508) 831-5640
Office Hours:
Mondays 2:00 - 3:00 pm,
Thursdays 3:00 - 4:00 pm
or by appointment .

TEACHING AND SENIOR ASSISTANTS:

Messages sent to cs444x_ta@cs.wpi.edu reach both the instructor and the TAs/SA.


TEXTBOOK:

Several other books on the subject and related subjects are recommended below. Some research papers will be handed out during the term.

GRADES:

Exam 1 25%
Exam 2 25%
Project/Homework 1 12.5%
Project/Homework 2 12.5%
Project/Homework 3 12.5%
Project/Homework 4 12.5%
Class Participation and Pop Quizzes Extra Points

Your final grade will reflect your own work and achievements during the course. Any type of cheating will be penalized with an NR grade for the course and will be reported to the WPI Judicial Board in accordance with the Academic Honesty Policy.

According to the WPI Undergraduate Catalog, "Unless otherwise indicated, WPI courses usually carry credit of 1/3 unit. This level of activity suggests at least 17 hours of work per week, including class and laboratory time." Hence, you are expected to spend at least 13 hours of work per week on this course outside the classroom.


BS/MS GRADUATE CREDIT

This course may be taken for graduate credit by students in the BS/MS CS program. Written permission from the professor is required. In order to receive graduate credit, students who have signed up for this program need to work on projects/homework alone (that is, in groups of 1 student :-)

EXAMS

Format
There will be a total of 2 exams. Each exam will cover the material presented in class since the beginning of the term. In particular, the final exam is cumulative. Exams will be in-class, 50 minute, closed-book, individual exams. Collaboration or other outside assistance on exams is not allowed.

Makeups
Regarding makeup exams, I follow Prof. Gennert's policy: "Makeup and/or early examinations are not given except under the most dire of circumstances, and then only with corroborating documentation. Note well that neither oversleeping, forgetting to show up for an exam, nor conflicting travel arrangements are considered dire circumstances."


PROJECTS/HOMEWORK

There will be a total of 4 projects/homework. Each of the projects deals with one of the data mining techniques covered in the class.
Data Mining Tool
For most of the projects, we will use the
Weka system (http://www.cs.waikato.ac.nz/ml/weka/). Weka is an excellent machine-leaning/data-mining environment. It provides a large collection of Java-based mining algorithms, data preprocessing filters, and experimentation capabilities. Weka is open source software issued under the GNU General Public License. For more information on the Weka system, to download the system and to get its documentation, look at Weka's webpage (http://www.cs.waikato.ac.nz/ml/weka/). You should download and use the 3.2.3 GUI version of the system.
Teams
Students are expected to organize themselves into groups of exactly 2 for each of the projects/homework, except for students taking this course for BS/MS credit who are expected to work on the projects/homework alone.
Submissions and Late Policy
See each project statement for details.
Project Descriptions
More detailed descriptions of the projects/homework will be posted to the course webpage at the appropriate times during the term. Although you may find similar programs/systems available online or in the references, the design and all code you use and submit, the results, and the analysis of the results in your projects/homework submissions MUST be your own original work.

CLASS PARTICIPATION

Students are expected to read the material assigned for each class in advance and to participate in class discussions. Class participation will be taken into account when deciding students' final grades.

CLASS MAILING LIST

There are two mailing lists for this class: cs444x@cs.wpi.edu and cs444x_ta@cs.wpi.edu:

CLASS WEB PAGES

The web pages for this class are located at http://www.cs.wpi.edu/~cs444x/d03/
Announcements will be posted on the web pages and/or the class mailing list, and so you are urged to check your email and the class web pages frequently. 

ADDITIONAL REFERENCES

Knowledge Discovery and Data Mining

Machine Learning

General AI

Databases

Statistics


OTHER ONLINE RESOURCES:

Data Sets

KDD

KDD Commercial Products / Prototypes

Data Warehousing and OLAP

Machine Learning

Statistics

General AI