Information Systems Engineering
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Ders Genel Tanıtım Bilgileri

Course Code:

FET404

Ders İsmi:

Artificial Intelligence Mathematics

Ders Yarıyılı:

Fall

Ders Kredileri:

Theoretical	Practical	Laboratory	ECTS
2	1	0	5

Language of instruction:

Turkish

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Yes

Type of course:

Bölüm Seçmeli

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator :

Asst. Prof. Dr. TAYMAZ AKAN

Course Lecturer(s):

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:

The aim of this course is to teach students the mathematical foundations of artificial intelligence algorithms, with a focus on metaheuristic optimization techniques. The course aims to provide both theoretical and practical knowledge on the classification, evaluation, and application of such algorithms.

Course Content:

Fundamentals of metaheuristic optimization algorithms

Types of algorithms: binary, continuous, discrete

Termination criteria and performance evaluation metrics

Exploration vs. exploitation balance

Concepts of unimodal, multimodal, and multi-objective optimization problems

Sample algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Battle Royale Optimization

Standard benchmark functions

Implementation of algorithms in Python or similar environments

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Develops comprehensive theoretical knowledge on the classification, mathematical foundations, and parameter sensitivity of metaheuristic optimization algorithms, and uses this knowledge to analyze the structure of AI-based decision-making problems.
2 - Skills
Cognitive - Practical
1) Selects appropriate algorithms for diverse optimization problems (binary, continuous, discrete, multimodal) based on problem definition, search space, objective function, and multi-objectivity; comparatively evaluates their exploration and exploitation behavior.
3 - Competences
Communication and Social Competence
Learning Competence
1) Critically follows emerging research topics in AI optimization, analyzes how algorithms evolve across different application domains, and reconstructs their own learning process accordingly.
Field Specific Competence
1) Evaluates optimization algorithms on multi-objective, high-dimensional, and benchmark-based AI problems, and discusses algorithmic effectiveness in terms of computational complexity, convergence speed, and overall solution quality from a technical perspective.
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Introduction to AI and Optimization Overview of optimization in AI; heuristic and metaheuristic methods are introduced.	Lecture Notes
2)	Structure of Metaheuristic Algorithms Explains population representation, initialization strategies, and fitness evaluation.	Lecture Notes
3)	Search Strategies: Exploration vs Exploitation Local and global search concepts; the balance of exploration within the search space is discussed.	Lecture Notes
4)	Algorithm Types: Binary, Continuous, Discrete Theoretically analyzes algorithm structures suitable for different problem types.	Lecture Notes
5)	Problem Characteristics Discusses variations in solution strategies depending on the nature of the problem (unimodal, multimodal, multi-objective).	Lecture Notes
6)	Particle Swarm Optimization (PSO) Presents the mathematical model of PSO, detailing velocity and position update equations.	Lecture Notes
7)	Genetic Algorithms (GA) Selection, crossover, and mutation operations are discussed from a theoretical perspective.	Lecture Notes
8)	Mid term	Lecture Notes
9)	PSO Implementation in Python Implements PSO on simple benchmark functions using Python.	Lecture Notes
10)	GA Implementation in Python Applies genetic algorithms to binary and continuous problems.	Lecture Notes
11)	Battle Royale Optimization (BRO) Explains unique characteristics of BRO, its social modeling approach, and update mechanisms.	Lecture Notes
12)	BRO Implementation in Python Implements and tests the BRO algorithm using Python.	Lecture Notes
13)	Comparison of Algorithms Compares GA, PSO, and BRO across multiple benchmark functions.	Lecture Notes
14)	Stopping Criteria and Evaluation Metrics Introduces metrics such as iterations, tolerance, error bounds, and success rates.	Lecture Notes
15)	Final	Lecture Notes
15)	Generalization and Adaptation to Real Applications Discusses the reusability of developed code across different real-world problems.
15)	Generalization and Adaptation to Real Applications Discusses the reusability of developed code across different real-world problems.

Sources

Course Notes / Textbooks:	Yapay Zeka Optimizasyon Algoritmaları (Derviş Karaboğa)
References:	Particle Swarm Optimization". Proceedings of IEEE International Conference on Neural Networks. Vol. IV. pp. 1942–1948 Genetic programming : an introduction on the automatic evolution of computer programs and its applications Yapay Zeka Optimizasyon Algoritmaları (Derviş Karaboğa)

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	3	4
Program Outcomes
1) Adequate knowledge in the fields of mathematics and science; ability to use theoretical and practical knowledge in these fields
2) Adequate knowledge in subjects specific to the relevant engineering discipline; ability to use theoretical and applied knowledge in these areas to solve complex engineering problems.
3) Ability to identify, formulate and solve complex engineering problems.
4) Ability to select and apply appropriate analysis and modeling methods to complex engineering problems.
5) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements.
6) Ability to apply modern design methods to design a complex system, process, device or product.
7) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering practice.
8) Ability to use information technologies effectively to analyze and solve complex problems encountered in engineering applications.
9) Ability to design and conduct experiments to investigate complex engineering problems or discipline-specific research topics.
10) Ability to collect data, analyze and interpret results for the investigation of complex engineering problems or discipline-specific research topics.
11) Ability to work effectively in disciplinary teams.
12) Ability to work effectively in multidisciplinary teams.
13) Ability to work individually.
14) Ability to communicate effectively both orally and in writing.
15) Knowledge of at least one foreign language.
16) Effective report writing and comprehension of written reports, ability to prepare design and production reports.
17) Ability to make effective presentations, give and receive clear and understandable instructions.
18) Awareness of the necessity of lifelong learning.
19) Ability to access information, to follow developments in science and technology and to continuously renew oneself.
20) Knowledge about acting in accordance with ethical principles, professional and ethical responsibility and standards used in engineering practices.
21) Knowledge of business practices such as project management, risk management and change management.
22) Awareness about entrepreneurship and innovation.
23) Knowledge about sustainable development.
24) Knowledge about the effects of engineering applications on health, environment and safety in universal and social dimensions and the problems of the era reflected in the field of engineering.
25) Awareness of the legal implications of engineering solutions.

Ders - Öğrenme Kazanımı İlişkisi

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

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in the fields of mathematics and science; ability to use theoretical and practical knowledge in these fields	1
2)	Adequate knowledge in subjects specific to the relevant engineering discipline; ability to use theoretical and applied knowledge in these areas to solve complex engineering problems.	4
3)	Ability to identify, formulate and solve complex engineering problems.	4
4)	Ability to select and apply appropriate analysis and modeling methods to complex engineering problems.	2
5)	The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements.	4
6)	Ability to apply modern design methods to design a complex system, process, device or product.	4
7)	Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering practice.	2
8)	Ability to use information technologies effectively to analyze and solve complex problems encountered in engineering applications.	2
9)	Ability to design and conduct experiments to investigate complex engineering problems or discipline-specific research topics.
10)	Ability to collect data, analyze and interpret results for the investigation of complex engineering problems or discipline-specific research topics.	4
11)	Ability to work effectively in disciplinary teams.
12)	Ability to work effectively in multidisciplinary teams.
13)	Ability to work individually.	3
14)	Ability to communicate effectively both orally and in writing.
15)	Knowledge of at least one foreign language.
16)	Effective report writing and comprehension of written reports, ability to prepare design and production reports.
17)	Ability to make effective presentations, give and receive clear and understandable instructions.
18)	Awareness of the necessity of lifelong learning.	3
19)	Ability to access information, to follow developments in science and technology and to continuously renew oneself.	3
20)	Knowledge about acting in accordance with ethical principles, professional and ethical responsibility and standards used in engineering practices.
21)	Knowledge of business practices such as project management, risk management and change management.	4
22)	Awareness about entrepreneurship and innovation.
23)	Knowledge about sustainable development.
24)	Knowledge about the effects of engineering applications on health, environment and safety in universal and social dimensions and the problems of the era reflected in the field of engineering.
25)	Awareness of the legal implications of engineering solutions.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100