Akademik Dersler - Tanıtım


Code - Course Title	ENM612 - Data Mining with Mathematical Programming
Course Type	Elective Courses
Language of Instruction	Türkçe
Laboratory + Practice	3+0
ECTS	7.5
Course Instructor(s)	PROFESÖR DOKTOR GÜRKAN ÖZTÜRK
Mode of Delivery	Face to face
Prerequisites
Courses Recomended	Operations Research, Computer Programming
Required or Recommended Resources	Tan, P.N., Steicnbach, M., Kumar, V. (2005), Introduction to Data Mining, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USAAlpaydin E., (2004), Introduction to Machine Learning, MIT Press, Cambridgehttp://www-users.cs.umn.edu/~kumar/dmbook/index.php
Recommended Reading List
Assessment methods and criteria
Work Placement
Sustainability Development Goals

Weeks	Topics
Week - 1	What is Data Mining?
Week - 2	Data Mining Problems: Clustering, Classification and Association analysis.
Week - 3	Clustering problems and solution approaches.
Week - 4	Mathematical Programming for solving clustering problems
Week - 5	Classification problems and solution approaches
Week - 6	Mathematical programming for solving classification problems
Week - 7	Clustering approaches based on hyperplanes ( RLP, h-polyhedral, etc)
Week - 8	Support Vector Machines
Week - 9	Classification approaches based on Polyhedral Conic Functions
Week - 10	Polyhedral conic functions algorithm
Week - 11	Integer programming PCF model
Week - 12	K-means based PCF-RLP for solving large scale classification problems
Week - 13	Association Analysis and solution approaches
Week - 14	Mathematical programming based solution approaches for solving association analysis problem

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
1.Midterm Exam	1	20
Homework	1	20
Final Exam	1	60
Toplam (%)		100