CS 9633

Knowledge Discovery and Data Mining

Syllabus

Instructor: Dr. Susan Bridges Office: 312 Butler Hall

Email: Bridges@cs.msstate.edu Phone: 325-7505

Office hours: M 1:30 - 3:00 Prerequisite: CS 4633/6633

W 10:00 – 11:30 Artificial Intelligence

Textbook: Data Mining: Concepts and Techniques by Jiawei Han and Micheline Kamber

Plus papers which will be distributed in class

Grade determination:

exams (midterm and final) 40% (20% each)

projects 25%

term paper 15%

oral presentation of paper 10%

class participation 10% (includes discussion of assigned papers)

Both the midterm exam and the final exam will be take-home exams. The final exam will be handed out in class on Thursday, April 8; it will be due by 3:00 p.m. on Saturday, May 5. Students should be aware that the "working time" for the final exam will include "dead days" so that they can arrange their study schedules in advance. Neither the final exam nor the midterm exam will be accepted after their due dates. Each student is required to give an oral presentation of his/her term paper to the class. The presentations should last 20-25 minutes, after which members of the audience may ask questions. Failure to stay within the time constraints will result in a lower grade.

Grading scale:

90 - 100 A

80 - 89 B

70 - 79 C

60 - 69 D

0 - 59 F

Class Policies

1. No extra credit will be assigned on an individual basis.

2. Cheating on an exam will result in an F for the course as well as possible university disciplinary measures.

A student is to consult no one other than the instructor of this course for help on an exam. A student must cite any references used in answering exam questions or resources used to complete projects and term papers. Consulting anyone other than the instructor and/or failing to cite references constitute cheating on an exam.

3. The projects and the term paper are individual endeavors. Students may consult only with the instructor for help on these assignments. Cheating on the assignments will result in a grade of F for the course as well as possible university disciplinary measures.

4. Students must satisfy the departmental policy regarding academic dishonesty (see below) as well as specific instructions for this course (see items 2 and 3 above). The Computer Science Departmental Policy Regarding Academic Honesty can be found at http://www.cs.msstate.edu/academics/honesty.html

5. The University Add/Drop Policy will be followed in this class. The policy can be found at www.cs.msstate.edu/academics/add_drop.html