CS 484 Syllabus

CS484
Data Mining(Fall 2008)

Meeting Time and Location:
M and W 1:30 pm - 2:45 pm , Innovation Hall 134

Instructor: Prof. Daniel Barbará.
Email: dbarbara (at) gmu (dot) edu
Office: STII 353
Office hour: by appointment

Graduate TA: TBA

Course Home Page
CS484

Overview
This course provides an introduction to the fundamental concepts in Data Mining

Course Outcomes:

The ability to apply computing principles, probability and statistics relevant to the data mining discipline to analyze data.
A thorough understanding of model programming with data mining tools, algorithms for estimation, prediction, and pattern discovery.
The ability to analyze a problem, identifying and defining the computing requirements appropriate to its solution: data collection and preparation, functional requirements, selection of models and prediction algorithms, software, and performance evaluation.
The ability to understand performance metrics used in the data mining field to interpret the results of applying an algorithm or model, to compare methods and to reach conclusions about data.
The ability to communicate effectively to an audience the steps and results followed in solving a data mining problem (through a term project)

Prerequisites: grade of C or better in CS 310 and STAT 344, or permission of instructor Students not satisfying the prerequisites will be dropped from the class.

Textbook

Data Mining: Introduction to Data Mining by Tan, Steinbach and Kumar

Class Attendance
Required. Please arrive on time. I expect to start at 1:30 sharp; Please participate in class! Ask questions if there is something you don't understand.
Grading Policies
There will also be written homeworks, a final project, a in-class midterm exam, and a final exam. Both the final and midterm are open-book and open-notes. The final exam will be comprehensive, i.e., it will cover the entire course. Missed exams must be arranged with the instructor BEFORE the exam. Documentation of the illness (doctor's note) is required. No early exams will be given and make-up exams are strongly discouraged.
End-of-semester numeric scores will be weighted as follows (tentative plan):

25% Assignments
45% Exams

20% midterm
25% final

30% project
In order to obtain an A, your final score should be at least 90. A total score of 49 or less will result in an F.
Late Policy
Late homework will be accepted with a penalty of 20% per day within 3 days after deadlines and will not be accepted three days after due, unless under prearranged conditions.
Honor Code
You are expected to abide by the honor code. All assignments and exams are individual efforts. Please refer to GMU Academic Policies and Computer Science Department Honor Code. Any violation of the honor code will result in a zero of the assignment/exam, and may result in an F for the class.
School Calendar

Disability

CS484 Data Mining(Fall 2008)

CS484
Data Mining(Fall 2008)