Computer Engineering

Course Code	Course Name	Year	Period	Semester	T+A+L	Credit	ECTS
05081105	Optimization Tecnics	2025	Spring	8	3+0+0	3	5

Course Type	Elective
Course Cycle	Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6)
Course Language	Turkish
Methods and Techniques	-
Mode of Delivery	Face to Face
Prerequisites	-
Coordinator	-
Instructor(s)	Asst. Prof. Neşe ÖZKAN YILMAZ
Instructor Assistant(s)	-

Basic Vocational Courses
Specialization / Field Courses
Support Courses	X
Transferable Skills Courses
Humanities, Communication and Management Skills Courses

Relationship Levels
Lowest	Low	Medium	High	Highest
1	2	3	4	5

#	Program Learning Outcomes	Level
P1	Adequate knowledge in mathematics, science and related engineering discipline accumulation; theoretical and practical knowledge in these areas, complex engineering the ability to use in problems.	1
P2	Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose	2
P4	Ability to develop, select and use modern techniques and tools for the analysis and solution of complex problems encountered in engineering applications; ability to use information technologies effectively	3

Upon the successful completion of this course, students will be able to:
No	Learning Outcomes	Outcome Relationship	Measurement Method **
O1	Must have technological knowledge of basic measurement theory, sensors and other measurement components	P.4.1	1
O2	Determining the talent required to solve problems and developing the necessary method.	P.4.2	1
O3	Application of developed methods to solve problems effectively and efficiently.	P.4.3	2
** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7

Week	Topics
1	Fundamentals of Optimization
2	Linear Programming
3	Nonlinear Programming
4	Network Flow Problems and Algorithms
5	Meta-heuristic Algorithms
6	Genetic Algorithms
7	Ant colony optimization
8	Stochastic Optimization
9	Multi-Objective Optimization
10	Game Theory and Optimization
11	Optimization Tools
12	Applications of optimization techniques in various fields
13	Applications of optimization techniques in various fields
14	Applications of optimization techniques in various fields

Resources

Lecture notes

In-Term Studies	Quantity	Percentage
Attendance	-	-
Laboratory	-	-
Practice	-	-
Course Specific Internship (If Any)	-	-
Homework	-	-
Presentation	-	-
Projects	-	-
Quiz	-	-
Midterms	1	40 (%)
Final Exam	1	60 (%)
Total		100 (%)

	Quantity	Duration	Total Work Load
Course Week Number and Time	14	3	42
Out-of-Class Study Time (Pre-study, Library, Reinforcement)	14	3	42
Midterms	1	32	32
Quiz	0	0	0
Homework	0	0	0
Practice	0	0	0
Laboratory	0	0	0
Project	0	0	0
Workshop	0	0	0
Presentation/Seminar Preparation	0	0	0
Fieldwork	0	0	0
Final Exam	1	34	34
Other	0	0	0
Total Work Load:			150
Total Work Load / 30			5
Course ECTS Credits:			5

Relationship Levels
Lowest	Low	Medium	High	Highest
1	2	3	4	5

#	Learning Outcomes	P4
O1	Must have technological knowledge of basic measurement theory, sensors and other measurement components	1
O2	Determining the talent required to solve problems and developing the necessary method.	2
O3	Application of developed methods to solve problems effectively and efficiently.	3