Syllabus

**Class Information**
Instructor:	Huzefa Rangwala, Room #345 ST II, rangwala@cs.gmu.edu
Class Time & Location:	IH 105, M 4:30pm - 7:10pm
Text Book:	Pang-Ning Tan, Michael Steinbach, and Vipin Kumar Introduction to Data Mining, Addison Wesley, 2006. Book's companion website
Teaching Assistant:	Al-Arayed, Dalal , 330/348 ST II, dalaraye@gmu.edu
Office Hours:	Instructor: M 3:00-4:00 pm, TA: W: 2:30-4:30pm

Please note the syllabus is subject to change to enrich the student's learning experience :). Feel free to email rangwala@cs.gmu.edu for questions, concerns, or even say hi

**About the Course**
Course Description
Over the past decade there has been an exponential increase in the amount of data. This has lead to development of techniques to discover useful and interesting information from the large collections of data. This course aims to provide a overview of the key data mining methods and techniques like classification, clustering, and association rule mining. The course will also provide interesting application examples of data mining, especially in the field of bioinformatics and spatial data mining.
Course Prerequisites
Some programming experience is expected. Students should be familiar with basic probability and statistics concepts, and linear algebra. Please expect some programming in the assignments and class projects.
Course Format
Lectures will be given by the instructor. Besides material from the textbook, topics not discussed in the book may also be covered. Research papers and handouts of material not covered in the book will be made available. Grading will be based on homework assignments, exams, and a project. Homework assignments will require some programming. Exams and homework assignments must be done on an individual basis. Any deviation from this policy will be considered a violation of the GMU Honor Code.
Course Outcomes
As an outcome of taking this class, a student will be able to Understand the various classification, clustering, association rule-mining algorithms. Apply the data mining techniques learned to real world applications. Read research papers pertaining to data mining.

**Tentative Class Schedule**
Date	Topics Covered
08.25.2008	Welcome, Introduction to Data Mining (Chapter 1) Data (Chapter 2)
09.01.2008	Labor Day (No Class)
09.08.2008	Data Contd. (Chapter 2) Warehousing and Exploration (Chapter 3)
09.15.2008	Classification (Chapter 4) Classification (Chapter 5)
09.22.2008	JIgsaw Activity (Classification Algorithms) Bioinformatics Example (Fold Recognition)
09.29.2008	Clustering (Chapter 7)
10.6.2008	Mid Term 1 Clustering (Chapter 8)
10.13.2008	Clustering ( Chapter 8)
10.20.2008	Association Rule Mining (Chapter 6)
10.27.2008	Outlier Detection Mining (Chapter 10)
11.3.2008	Outlier Detection Mining (Chapter 10)
11.10.2008	Data Mining in Bioinformatics
11.17.2008	Mid Term 2 Spatial Data Mining
11.24.2008	Project Presentations I
12.01.2008	Guest Lecture (Dr. Carlotta)
12.08.2008	Project Presentations II
12.15.2008	No Final :)

**Assignments/Exams**
Deadline	Type	% Weight
09.22.2008	Assignment 1	10
10.6.2008	Mid-Term Exam 1	20
10.13.2008	Assignment 2	10
11.10.2008	Assignment 3	10
11.17.2008	Mid-Term Exam 2	20
12.08.2008	Final Project* (See Below for Milestones)	30
10.20.2008	Project Proposal (2 pages)	5
Signup	Project Presentations	10
12.08.2008	Final Report	15

* Note: There will be no extensions offered for the project. I would encourage you to start early.

**Grade Distribution**
Grade	Score Range
A+	>97
A	92-97
A-	88-92
B+	84-88
B	80-84
B-	76-80
C+	72-76
C	68-72
C-	64-68
D+	60-64
D	56-60
D-	52-56
F	< 50

**Policies:**
Attendance
Attendance is not compulsory but highly recommended for doing well in the class. This class has lots of active learning exercises, and they will be a lot of fun.
Assignment Submission
Please ensure that the assignments are submitted on-time. No late submissions.
Make-Up Exams & Incompletes
Make up exams and incompletes will not be given for this class.
Academic Honesty and GMU Honor Code
Please visit the University's Academic Honesty Page and GMU Honor Code .
Disability Statement
If you have a documented learning disability or other condition that may affect academic performance you should: 1) make sure this documentation is on file with the Office of Disability Services (SUB I, Rm. 222; 993-2474; www.gmu.edu/student/drc) to determine the accommodations you need; and 2) talk with me to discuss your accommodation needs.