CSCI 4380/6380 Data Mining

Spring 2025: Tuesdays and Thursdays 12:45pm - 2:00pm, Science Learning Center Room 345
& Wednesdays 12:40pm - 1:30pm, Chemistry Building Room 674

Instructor: Prof. Khaled Rasheed
Telephone: (706)542-0881
Office Hours: Thursday 2:30-4:30pm or by email appointment
Office Location: Room 518, Boyd GSRC
Email: khaled@uga.edu

Teaching Assistant: To be announced

Objectives:

The course aims to provide students with a broad introduction to the field of Data Mining and related areas and to teach students how to apply these methods to solve problems in complex domains. The course is appropriate both for students preparing for research in Data Mining and Machine Learning, as well as Bioinformatics, Science and Engineering students who want to apply Data Mining techniques to solve problems in their fields of study.

Recommended Background:

CSCI 2720 Data Structures. Familiarity with basic computer algorithms and data structures. Knowledge of a modern programming language.

Topics to be Covered:

Part I: Data Mining techniques: Selected from: Association and Classification Rule Mining, Linear Models, Decision Trees and Random Forests, Neural Network approaches, Support Vector Machines, Bayesian Learning, Instance-based Learning, Pre-processing and Feature Selection, Performance evaluation, Ensemble Learning and clustering.

Part II: Data Mining applications: Selected from: Bioinformatics, Biomedical/Physical/Chemical modeling, medical diagnosis, text/web mining, pattern recognition and/or other contemporary applications.

Expected Work:

Reading; assignments (include running experiments using the Weka package); paper presentation, two midterms; and term project (may require programming or running existing packages) and paper. Unless otherwise announced by the instructor, all assignments and all exams must be done entirely on your own.

Academic Honesty and Integrity:

All academic work must meet the standards contained in "A Culture of Honesty." Students are responsible for informing themselves about those standards before performing any academic work. The penalties for academic dishonesty are severe and ignorance is not an acceptable defense.

Grading Policy:

Assignments: 30% (Programs, homeworks, attendance, paper presentation)

Midterm Examinations: 40%

Term Project: 30% (includes term paper and presentation)
Students may work on their term projects in groups of up to FOUR students each. The above distribution is only tentative and may change later. The instructor will announce any changes.

Assignment Submission Policy

Assignments must be turned in by the assigned deadline on eLC. Late assignments will lose 10% for every calendar day. Rare exceptions may be made by the instructor only under extenuating circumstances and in accordance with the university policies.

Course Home-page

A variety of materials will be made available on the DM Class Home-page at https://khaledmrasheed.github.io/DMcourse/, including handouts, lecture notes and assignments. Announcements may be posted between class meetings. You are responsible for being aware of whatever information is posted there.

Lecture Notes

Copies of Dr. Rasheed's lecture notes will be available on eLC and at the bottom of the class home page. Not all the lectures will have electronic notes though and the students should be prepared to take notes inside the lecture at any time.

Textbook in Bookstore

"Data Mining: Practical Machine Learning Tools and Techniques (4th edition)", Ian Witten, Eibe Frank , Mark Hall and Christopher Pal. Morgan Kaufmann, 2016. (Required)
ISBN-10: 0128042915 & ISBN-13: 978-0128042915

Web Resources

The WEKA Machine Learning Project

University of California at Irvine ML Repository

Announcements:

Papers:

Assignments:

Lecture Notes:

Chapter 1

Chapter 2

Chapter 3

The course syllabus is a general plan for the course; deviations announced to the class by the instructor may be necessary.

Last modified: January 15, 2025.

Khaled Rasheed (khaled[at]uga.edu)