BİLİŞİM GÜVENLİĞİ TEKNOLOJİSİ
Qualification Awarded	Program Süresi	Toplam Kredi (AKTS)	Öğretim Şekli	Yeterliliğin Düzeyi ve Öğrenme Alanı
Associate (Short Cycle) Degree	2	120	FULL TIME	TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 5 QF-EHEA:Short Cycle TR-NQF-HE, ISCED (1997-2013): 48,52

Ders Genel Tanıtım Bilgileri

Course Code:

2000002013

Ders İsmi:

Artificial Intelligence

Ders Yarıyılı:

Spring

Ders Kredileri:

Theoretical	Practical	Labs	Credit	ECTS
3	0	0	3	3

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Other Recommended Topics for the Course:

Type of course:

Bölüm Seçmeli

Course Level:

Associate

TR-NQF-HE:5. Master`s Degree

QF-EHEA:Short Cycle

EQF-LLL:5. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Öğr.Gör. Serhat DALGALIDERE

Course Lecturer(s):

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:

The aim of this course is to provide students with an introduction to artificial intelligence, including the basic techniques and mechanisms of artificial intelligence. It is aimed that the students who complete this course will understand the historical and conceptual development of artificial intelligence, the aims of artificial intelligence and the methods it uses to achieve these goals, the social and economic role of artificial intelligence, and by analyzing the problems, determining where artificial intelligence techniques can be used and using artificial intelligence techniques.

Course Content:

Introduction to artificial intelligence, Natural and Artificial Intelligence, Turing Test, Search methods, Planning, Heuristic Problem Solving, Information representation, Predicate Logic, Artificial Intelligence Programming Languages, Programming with Common Lisp, Game Theory, Genetic Algorithms, Fuzzy Logic, Expert Systems, Artificial Intelligence Applications.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Learning Artificial Intelligence concepts
2 - Skills
Cognitive - Practical
1) Understanding the application areas
3 - Competences
Communication and Social Competence
1) Reinforcement by doing practical work on a sample project
Learning Competence
1) To understand how to construct the algorithm in all system studies.
Field Specific Competence
1) Creating demos to work on artificial intelligence
Competence to Work Independently and Take Responsibility
1) To be able to create system analysis and set up a programming scheme

Ders Akış Planı

Week	Subject	Related Preparation
1)	Meeting students and explaining the content of the course.
2)	Artificial Intelligence Definition, History, Development and Application Areas
3)	Fuzzy Logic: Expert Systems and Controllers
4)	The Relationship of Artificial Intelligence with Humans
5)	Aristotelian Logic, Fuzzy Logic. Comparison of two logics
6)	Artificial Intelligence Models
7)	Decision Tree Algorithms
8)	Midterm Exam-1
9)	Intelligent Agents, Natural Language Processing
10)	Big Data, Machine Learning
11)	Algorithms and Rational Artificial Intelligence
12)	Midterm Exam-2
13)	Types of Artificial Intelligence
14)	Areas of Use of Artificial Intelligence and Sustainability
15)	Fınal Exam

Sources

Course Notes / Textbooks:	Ders Notları
References:	1.Vasif Nabiyev, Yapay Zeka, 5. Baskı, Seçkin Yayınevi 2.Yapay Zeka Ders Notu, Cahit Karakuş, 2023. https://ckk.com.tr/ders/YZ/YZ%2000%20Yapay%20Zeka%20Ders%20Notu.html 3. Makine Öğrenmesinde Sınıflandırma Yöntemleri ve R Uygulamaları, Selçuk Alp, Ersoy Öz., Nobel Akademik Yayıncılık, 2019.

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	3	4	5	6
Program Outcomes
1) Having knowledge and skills in security algorithms for programming
1) Possesses in-depth theoretical knowledge in information security, cybersecurity, network security, and data protection.
2) Understands information security management systems, national and international security standards, ethical considerations, and legal regulations.
2) Ability to install and manage software required for end user security
3) Has analytical thinking skills in classifying cyber threats, attack vectors, and defense mechanisms.
3) Having the ability to install and manage computer networks and use the network operating system
4) Theoretically comprehends security technologies such as cryptography, digital signatures, authentication, and access control mechanisms.
5) Can establish network security policies and effectively manage security tools such as firewalls and intrusion detection/prevention systems.
6) Has expertise in applying cryptographic algorithms, using data encryption techniques, and analyzing cybersecurity intelligence.
7) Can conduct penetration tests and security assessments to identify system vulnerabilities and develop preventive measures.

Ders - Öğrenme Kazanımı İlişkisi

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

	Program Outcomes	Level of Contribution
1)	Having knowledge and skills in security algorithms for programming	2
1)	Possesses in-depth theoretical knowledge in information security, cybersecurity, network security, and data protection.	2
2)	Understands information security management systems, national and international security standards, ethical considerations, and legal regulations.
2)	Ability to install and manage software required for end user security	3
3)	Has analytical thinking skills in classifying cyber threats, attack vectors, and defense mechanisms.
3)	Having the ability to install and manage computer networks and use the network operating system	2
4)	Theoretically comprehends security technologies such as cryptography, digital signatures, authentication, and access control mechanisms.
5)	Can establish network security policies and effectively manage security tools such as firewalls and intrusion detection/prevention systems.
6)	Has expertise in applying cryptographic algorithms, using data encryption techniques, and analyzing cybersecurity intelligence.
7)	Can conduct penetration tests and security assessments to identify system vulnerabilities and develop preventive measures.

Öğrenme Etkinliği ve Öğretme Yöntemleri

Alan Çalışması
Anlatım
Bireysel çalışma ve ödevi
Course
Grup çalışması ve ödevi
Labs
Homework
Problem Çözme
Proje Hazırlama
Rapor Yazma
Rol oynama
Soru cevap/ Tartışma
Sosyal Faaliyet
Örnek olay çalışması
Web Tabanlı Öğrenme

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Sözlü sınav
Homework
Uygulama
Gözlem
Sunum
Bilgisayar Destekli Sunum
Örnek olay sunma

Assessment & Grading

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

İş Yükü ve AKTS Kredisi Hesaplaması

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	2	28
Study Hours Out of Class	14	2	28
Midterms	1	10	10
Final	1	10	10
Total Workload			76