CS 659: Theory and Applications of Data Mining

**Class Information**
Class/Sec:	CS 659 (002): Theory and Applications of Data Mining
Instructor:	Huzefa Rangwala [1], Room #4423 EB, rangwala@cs.gmu.edu [2]
Class Time & Location:	Robinson Hall B118, R 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 [3]
Teaching Assistant:	TBD (tbd@gmu.edu [4])
Office Hours:	Instructor: R 2:00-4:00 pm

Please note the syllabus is subject to change to enrich the student's learning experience :). Feel free to email rangwala@cs.gmu.edu [2] for questions, concerns, or even say hi.

If you have taken CS 750 (or INFS 755), then you will not receive credit for CS 659

**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, social network analysis and "big data".
Course Prerequisites
CS 580 + 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. If you are not sure about the pre-reqs send me an email.
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 programming.
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 and cloud computing

Schedule

01.24.2013	Welcome, Introduction to Data Mining (Chapter 1)
01.31.2013	Data (Chapter 2)	[HW1 out]
02.07.2013	Classification (Chapter 4 & 5)
02.14.2013	Classification (Contd)	[HW1 due]/[HW2 out]
02.21.2013	Clustering (Chapters 7 & 8)
02.28.2013	Clustering (Contd.)	[HW2 due]/[HW3 out]
03.07.2013	Clustering (contd)/Exam I [15%]
03.14.2013	Spring Break
03.21.2013	Association Rule Mining I.	[Proposal Due]
03.28.2013	Association Rule Mining II	[HW3 due]
04.04.2013	Recommender Systems/Social Media Analysis
04.11.2013	Exam II [15%]/ Bioinformatics Application
04.18.2013	Massive Data Mining
04.25.2013	Project Presentations [Could be posters]
05.02.2013	Project Presentations [Could be posters]
05.10.2013	Project Report Due Via Email (5:00pm EST)

**Assignments/Exams**
Deadline	Type	% Weight
02.14.2013	HW 1	10
02.28.2013	HW 2	10
03.28.2013	HW 3	15
03.07.2013	Exam I	15
04.11.2013	Exam II	15
-	Class Participation	0
	Project	35
03.21.2013	Project Proposal (2 pages)	5
	Project Presentations	10
05.10.2013 (5:00 pm EST)	Final Report	20

**Grade Distribution**
Grade	Score Range
A+	>98
A	94-98
A-	90-94
B+	86-90
B	82-86
B-	78-82
C+	74-78
C	70-74
C-	66-70
F	< 66

**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 are allowed.
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 [5] and GMU Honor Code [6].
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 [7]) to determine the accommodations you need; and 2) talk with me to discuss your accommodation needs.