BİLİŞİM GÜVENLİĞİ TEKNOLOJİSİ
Associate	TR-NQF-HE: Level 5	QF-EHEA: Short Cycle	EQF-LLL: Level 5

Ders Genel Tanıtım Bilgileri

Course Code:

2000002013

Ders İsmi:

Artificial Intelligence

Ders Yarıyılı:

Spring
Fall

Ders Kredileri:

Theoretical	Practical	Credit	ECTS
3	0	3	3

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

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. Yasemin GÜNTER

Course Lecturer(s):

Öğr.Gör. Yasemin GÜNTER

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)
2)	Expression of information	source scanning
3)	Intuition and Heuristic Search Algorithms	Source Scanning
4)	Game Theory, Game Tree	Source Scanning
5)	Genetic Algorithms	Source Scanning
6)	Fuzzy Logic	Source Scanning
7)	Logical Programming	Source Scanning
8)	midterm	Source Scanning
9)	Student Presentations	Source Scanning
10)	Student Presentations	Source Scanning
11)	Student Presentations
12)	Student Presentations
13)	Student Presentations
14)	Student Presentations
15)	Final 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
2) Ability to install and manage software required for end user security
3) Having the ability to install and manage computer networks and use the network operating system
4) Have basic database and web programming skills

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
2)	Ability to install and manage software required for end user security	3
3)	Having the ability to install and manage computer networks and use the network operating system	2
4)	Have basic database and web programming skills	2

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