Course Description
A broad introduction to data mining methods and an exploration of research problems in data mining and its applications in complex real-world domains. Approaches include association and classification rule learning, tree learning, neural network and Bayesian methods, support vector machines, clustering, and ensemble learning.
Additional Requirements for Graduate Students:
Each graduate student will present a recent research article to
the class and do extra project work. In the homework
assignments and exams, graduate students will be assigned
additional graduate-level questions. They will also be graded
using a stricter scale.
Athena Title
Data Mining
Undergraduate Prerequisite
CSCI 2720 or CSCI 2725
Graduate Prerequisite
CSCI 2720 or CSCI 2725
Semester Course Offered
Not offered on a regular basis.
Grading System
A - F (Traditional)
Course 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.
Topical Outline
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.
Syllabus