CS 584: Data Mining (Syllabus Page)

**Class Information**
Class/Sec:	CS 584: Theory and Applications of Data Mining
Instructor:	Huzefa Rangwala [1], Room #4423 EB, rangwala@cs.gmu.edu [2]
Class Time & Location:	Monday 4:30 - 7:10 pm Innovation Hall 134
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
Office Hours:	Instructor: Monday 2-4 pm in Engineering 4423.
Communication and Class Link:	Piazza Link: Piazza [4]
Automated Data Mining Hackathon Host:	Miner [5] (Only ON Campus or VPN)

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 659 or 750 (or INFS 755), then you will not receive credit for CS 584

**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 social media analysis, text analysis and learning analytics.
Course Prerequisites
Programming experience in Python Preferred. Java or C will work as well but Assignments will use Python framework. Students should be familiar with basic probability and statistics concepts, and linear algebra. Please expect programming in all 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 intensive programming using automated competition style solution development for data mining challenges. Exams and homework assignments must be done on an individual basis unless stated. 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 scientific and/or industrial applications.

Topics

Introduction

Data and It's Various Forms

Classification: Models, Methods and Applications

Clustering: Methods and Applications

Association Rule Mining

Applications: Biological Data Mining

Applications: Recommender Systems

Applications: Learning Analytics

Applications: Advanced Supervised Learning

Applications: Anomalies, Outliers

Detailed Schedule (Google iCal) https://calendar.google.com/calendar/ical/3udd0h0ge6sphklhcd9fd4ts54%40g... [6]

**Assignments/Exams**
Deliverable	Deadline	Grade Weights
HW0	Sep 12	0%
HW1	Sep 19	5%
HW2	Oct 3	10%
HW3	Oct 24	15%
HW4	Nov 14	10%
Mid-Term	Oct 17	15%
Project Pitch	Oct 31	0%
Project Proposal	Oct 31	5%
Project Presentation	Dec 5, 12	10%
Project Report	Dec 19 (No Final)	30%
Extra Credits: Participation, Competition Winners		5% (Max)

**Grade Distribution**
Grade	Score Range
A	>96
A-	92-96
B+	88-92
B	84-88
B-	80-84
C+	76-80
C	72-76
C-	68-72
F	< 68

**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. There will be several assignments and there may be dependencies amongst consecutive assignments.
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 [7] and GMU Honor Code [8].
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 [9]) to determine the accommodations you need; and 2) talk with me to discuss your accommodation needs.