Syllabus Application
CS 525
Data Mining
Faculty
Faculty of Engineering and Natural Sciences
Semester
Spring 2025-2026
Course
CS 525 -
Data Mining
Time/Place
Time
Week Day
Place
Date
12:40-13:30
Mon
FENS-L061
Feb 16-May 22, 2026
15:40-17:30
Tue
FENS-L027
Feb 16-May 22, 2026
Level of course
Masters
Course Credits
SU Credit:3, ECTS:10
Prerequisites
-
Corequisites
-
Course Type
Lecture
Instructor(s) Information
Yücel Saygın
- Email: ysaygin@sabanciuniv.edu
Course Information
Catalog Course Description
Data mining can be viewed as lossy data reduction and learning techniques that are designed to handle massive data sets containing large numbers of categorical and numeric attributes. This course covers topics in data mining and knowledge discovery structured and unstructured databases such as data integration, mining, and interpretation of patterns, rule-based learning, decision trees, association rule mining, and statistical analysis for discovery of patterns, evaluation and interpretation of the mined patterns using visualization techniques.
Course Learning Outcomes:
| 1. | list the basic components of a data mining process. |
|---|---|
| 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
? to introduce the basic data mining process which includes feature selection, supervised learning, unsupervised learning, association rule mining and inference
? to discuss the operations research background of popular hard and fuzzy clustering algorithms
? to derive multiple linear regression (MLR) as a tool in supervised learning process
? to introduce the operations research background of logistic regression as a classification algorithm.
? to introduce the details of popular classifiers such as naive bayes, instance based and decision tree based classifiers.
? to discuss the details of association rule mining.
? to introduce various intelligent search algorithms such as simulated annealing, genetic algorithms, tabu search, GRASP and beam search and how they are used as part of wrappers in the context of feature selection process.
? to introduce multicriteria decision making and how some MCDM algorithms can be incorporated in the data mining process
? to experience WEKA as a software tool in data mining.
? to expose the students to real world applications through case studies.
? to develop students? team working skills, as well as self-confidence, in dealing with decision making problems.
-
? to discuss the operations research background of popular hard and fuzzy clustering algorithms
? to derive multiple linear regression (MLR) as a tool in supervised learning process
? to introduce the operations research background of logistic regression as a classification algorithm.
? to introduce the details of popular classifiers such as naive bayes, instance based and decision tree based classifiers.
? to discuss the details of association rule mining.
? to introduce various intelligent search algorithms such as simulated annealing, genetic algorithms, tabu search, GRASP and beam search and how they are used as part of wrappers in the context of feature selection process.
? to introduce multicriteria decision making and how some MCDM algorithms can be incorporated in the data mining process
? to experience WEKA as a software tool in data mining.
? to expose the students to real world applications through case studies.
? to develop students? team working skills, as well as self-confidence, in dealing with decision making problems.