SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
BVA5107	Optimization Techniques	1	Fall	1	2	2	6

Course Type :	Compulsory
Cycle:	Associate TQF-HE:5. Master`s Degree QF-EHEA:Short Cycle EQF-LLL:5. Master`s Degree
Language of Instruction:	Turkish
Prerequisities and Co-requisities:	N/A
Mode of Delivery:	Face to face
Name of Coordinator:	Instructor ÖZGE DEMİR
Dersin Öğretim Eleman(lar)ı:	Instructor ÖZGE DEMİR
Dersin Kategorisi:	Programme Specific

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

This course covers mathematical modeling and solution techniques for decision-making problems encountered in big data environments. Students are introduced to optimization methods such as linear and nonlinear programming, integer programming, constraint programming, and heuristic algorithms. The course emphasizes the relationship between optimization and data analytics, machine learning, and operational decision support systems. It also introduces practical use of Python and similar programming languages, as well as optimization libraries (such as Pyomo, SciPy.optimize).

Course Content:

It covers mathematical models, methods and applications for finding the best solution under limited resources.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:



Knowledge *(Described as Theoritical and/or Factual Knowledge.)*
1) Understand the importance of optimization in machine learning algorithms
Skills *(Describe as Cognitive and/or Practical Skills.)*
1) Can apply linear and nonlinear optimization techniques
2) Can solve optimization problems with programming languages such as Python.
Competences *(Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)*
1) Can turn real-life problems into mathematical models.
2) Analyses that contribute to decision-making processes in the context of big data

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	What is Optimization? Basic Concepts
2)	Matematiksel Modelleme
3)	Linear Programming (LP)
4)	Linear Programming (LP)
5)	Graphical Method in LP Problems
6)	Simplex Method
7)	Duality and Sensitivity Analysis
8)	midterm exam
9)	Integer Programming
10)	Heuristic algorithm
11)	Genetic algorithm
12)	Multi-Objective Optimization
13)	Integer Programming
14)	İnteger programming

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)	1	4	2	3	5
Program Öğrenme Çıktıları (PÖÇ)
1) It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results.
2) It explains the principles of algorithm design and develops software for solving problems using at least one programming language.
3) It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data.
4) Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations.
5) They apply natural language processing techniques to text data and develop basic NLP-based applications.
6) It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools.
7) It creates data-driven decision models using decision support systems.
8) It develops optimization models and produces solutions for industrial and sectoral problems.
9) In professional practice, we operate within the framework of ethical principles, data security, and social responsibility.
10) They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness.

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

Level of Contribution of the Course to PLOs

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results.	2
2)	It explains the principles of algorithm design and develops software for solving problems using at least one programming language.	1
3)	It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data.	1
4)	Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations.	1
5)	They apply natural language processing techniques to text data and develop basic NLP-based applications.	1
6)	It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools.	1
7)	It creates data-driven decision models using decision support systems.	2
8)	It develops optimization models and produces solutions for industrial and sectoral problems.	5
9)	In professional practice, we operate within the framework of ethical principles, data security, and social responsibility.	1
10)	They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course
Midterm
Final Exam
Homework Evaluation

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Homework Assignments	1	% 15.00
Midterms	1	% 35.00
Semester Final Exam	1	% 50.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities	# of Activities per semester	Duration (hour)	Total Workload
Course	14	3	42
Laboratory	0	0	0
Application	0	0	0
Special Course Internship (Work Placement)	0	0	0
Field Work	0	0	0
Study Hours Out of Class	14	1	14
Presentations / Seminar	0	0	0
Project	0	0	0
Homework Assignments	1	15	15
Total Workload of Teaching & Learning Activities	-	-	71
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	0	0	0
Midterms	1	36	36
Semester Final Exam	1	46	46
Total Workload of Assesment & Evaluation Activities	-	-	82
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			153
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			6

Course Notes / Textbooks:
References:	Yapay Zeka Temelli Optimizasyon;Matlab ve Python UygulamalarıylaHacı Hasan Örkcü,Volkan Soner Özsoy,Emre Koçak Yöneylem Araştırması;Modeller, Yöntemler, ProblemlerHacı Hasan Örkcü,Volkan Soner Özsoy,Emre Koçak