| Course ID: | CSCI 4380/6380. 4 hours. |
| Course Title: | Data Mining |
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. |
| Oasis 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. |