CSCI 4380/6380 Data Mining

Spring 2026: Tuesdays and Thursdays 1:15pm - 2:35pm, Geography and Geology Room 200A
& Wednesdays 1:15pm - 2:10pm, Chemistry Building Room 674

Instructor: Prof. Khaled Rasheed
Office Hours: Wednesday 2:30-4:30pm or by email appointment
Office Location: Room 543, Boyd GSRC
Email: khaled@uga.edu

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

The Kaggle data science home

Announcements:

[2-16-2026] Course project signup link on https://docs.google.com/document/d/1QTRC2YvnQOmMQeYuLvZhV-KQc1CxI-c4YpkvWitMrTs/edit?usp=sharing

[2-20-2026] The first midterm exam will be on Thursday 2-26-2026. It will cover all the topics discussed in the course till the end of Chapter 4. It will be open notes but the use of books, laptops or phones will not be allowed. You should bring a calculator to the exam; If you do not have a calculator you may use your phone as a calculator after asking me for permission. You should also bring your lecture notes and all handouts and you may also bring any additional notes, homeworks etc. We shall have a review lecture on Wednesday 2-25-2026 in which we will go over the homework solutions and some additional problems from previous midterm exams.

Papers:

"A robust microbiome signature for autism spectrum disorder across different studies using machine learning" 2024. [Partha Koundinya Panguluri][4-7] {download}

"Unbiased split selection for classification trees based on the Gini Index" 2007. [Aiden King Benise][4-7] {download}

"Beyond Reality: The Pivotal Role of Generative AI in the Metaverse " 2023. [Joseph Vos][4-8] {download}

"Winner-takes-all for Multivariate Probabilistic Time Series Forecasting" 2025. [Firas Astwani][4-9] {download}

"Prediction and Prioritization of Rare Oncogenic Mutations in the Cancer Kinome Using Novel Features and Multiple Classifiers" 2014. [Pardis Sadatian][4-9] {download}

"Predicting Post Severity in Mental Health Forums" 2016. [Quentin Boccaleri][4-9] {download}

"DysLexML: Screening Tool for Dyslexia Using Machine Learning" 2019. [Tilak Savani][4-14]{download}

"Automated Classification of Text Sentiment" 2018. [Chongxin Zhong][4-14] {download}

"Application of data mining for young children education using emotion information" 2018. [Caleb Odunade][4-14] {download}

"An automated approach to predict diabetic patients using KNN imputation and efective data mining techniques" 2024. [Nikki Azadi][4-16] {download}

"Merging computational fluid dynamics and machine learning to reveal animal migration strategies" 2020. [Abigail Clark][4-21] {download}

"Motor Imagery EEG Signal Processing and Classification Using Machine Learning Approach" 2017. [?][?] {download}

"Text Similarity in Vector Space Models: A Comparative Study" 2019. [?][?] {download}

"Edge Machine Learning: Enabling Smart Internet of Things Applications" 2018. [?][?] {download}

"Determination of Flowing Grain Moisture Contents by Machine Learning Algorithms Using Free Space Measurement Data" 2022. [?][?] {download}

"Clustering cancer gene expression data: a comparative study" 2013. [?][?] {download}

"Forecast of the higher heating value based on proximate analysis by using support vector machines and multilayer perceptron in bioenergy resources" 2022. [?][?] {download}

"Web Application Attacks Detection Using Machine Learning Techniques" 2018. [?][?] {download}

Assignments:

Homework 1: Exercise 17.1 on pages 559 - 565 of the Weka exercises. You can download all the exercises from https://khaledmrasheed.github.io/DMcourse/Weka-Tutorial-Exercises.pdf. [Due 2-5-2026 on eLC] The use of Generaative AI is not allowed.

Homework 2

Course Project

Homework 3: Exercise 17.6 on pages 582 - 585 of the Weka exercises. You can download all the exercises from https://khaledmrasheed.github.io/DMcourse/Weka-Tutorial-Exercises.pdf. Include screen shots and answers to the questions. [Due 4-1-2026 on eLC]

Homework 4