Syllabus Application
Operations Research and Data Mining
IE 525
Faculty:
Faculty of Engineering and Natural Sciences
Semester:
Fall 2025-2026
Course:
Operations Research and Data Mining - IE 525
Classroom:
FENS-L027
Level of course:
Masters
Course Credits:
SU Credit:3.000, ECTS:10
Prerequisites:
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Corequisites:
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Course Type:
Lecture
Instructor(s) Information
Kemal Kılıç
- Email: kkilic@sabanciuniv.edu
Course Information
Catalog Course Description
The course will address unsupervised learning, supervised learning, association rule mining and feature subset selection problems, focus on the optimization formulations of these problems, discuss various techniques proposed as solutions and present their implementation particularly in the context of operations management. Among others, probabilistic and statistical methods, possibilistic methods clustering algorithms, decision trees, metaheuristics (such as genetic algorithms, simulated annealing, etc.) and mathematical programming will be covered as part of the toolbox that are widely utilized in data mining. As part of the course multi criteria decision making and multi objective optimization, and their usage in data mining will also be covered. The course will include case studies from both manufacturing and service industries.
Course Learning Outcomes:
| 1. | list the basic components of a data mining process. |
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| 2. | model a data mining problem and decide which techniques are suitable for the business objective of the user. |
| 3. | understand the optimization formulation of the various data mining problems and derive the techniques that can be used in those problems. |
| 4. | correctly apply the steps of various feature selection techniques, supervised and unsupervised learning algorithms and association rule mining algorithm. |
| 5. | implement feature selection, supervised and unsupervised learning algorithms, association rule mining techniques with a data mining tool (e.g., WEKA) in order to determine the relations that are hidden in the data. |
Course Objective
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Course Materials
Resources:
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Technology Requirements:
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