| WARNING: Small changes to this syllabus may be made during the semester. |
COURSE DESCRIPTION:This 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 studied.RECOMMENDED BACKGROUND:CS4341 Introduction to Artificial Intelligence, MA2611 Applied Statistics I, and CS3431 Database Systems I.
CLASS MEETING:Mondays, Tuesdays, Thursdays, Fridays 12:00-12:50 pmRoom: SL104 Please come to class on time and stay for the whole class period.
COURSE OBJECTIVES:
PROFESSOR:Prof. Carolina Ruiz
Office: FL 232 Phone Number: (508) 831-5640 Office Hours:
TEACHING ASSISTANT:
TEXTBOOK:
GRADES:
Your final grade will reflect your own work and achievements during the course. Any type of cheating will be reported to the WPI Judicial Board and penalized 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 CREDITThis 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).EXAMSFormatThere 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. The exams are scheduled for Monday, Nov. 20 and for Tuesday, Dec. 12th, 2006
MakeupsRegarding 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 & HOMEWORKThere 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 ToolFor 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 3-4-8a GUI version of the system.TeamsStudents are expected to organize themselves into groups of exactly 2 for each of the projects, except for students taking this course for BS/MS credit who are expected to work on the projects alone. Each project will contain both an individual assignment and a group assignment. Groups need not be the same for all projects.Submissions and Late PolicySee each project statement for details.Project DescriptionsMore 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 PARTICIPATIONStudents 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 LISTS AND myWPIThere are two mailing lists for this class (replace XXXX with 4445 below):
There is also a myWPI account for this class that will be used for project submissions only, as needed. CLASS WEB PAGESThe web pages for this class are located at http://www.cs.wpi.edu/~cs4445/b06/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. WARNING:Small changes to this syllabus may be made during the course of the term.ADDITIONAL REFERENCESKnowledge Discovery and Data Mining
Machine Learning
General AI
Databases
Statistics
OTHER ONLINE RESOURCES:Previous offerings of CS4445Webpages of my previous offerings of this course have plenty of useful resources: practice exams, exams, homework, solutions of those exams/hw, etc.Data Sets
KDD
KDD Commercial Products / PrototypesData Warehousing and OLAP
Machine Learning
StatisticsGeneral AI |
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