Course Title Course Code Program Level

Course Term
(Course Semester)
Teaching and Learning Methods
Theory Practice Lab Projects/Field Work Seminars/Workshops Other Total Credits ECTS Credits
42 30 53 125 3 5

Teaching Staff
Language of Instruction Türkçe (Turkish)
Type Of Course Elective
Recommended Optional Programme Component
Course Objectives With this course, the student will comprehend the sub-technology fields that make up the artificial intelligence world and the basic concepts, algorithms and approaches in these fields, and will have a vision for the areas where artificial intelligence can be applied in the future.
Course Content In this course, it is aimed to examine the classical logic, first degree logic and fuzzy logic fields, information security and cryptology disciplines, expert systems, tree and graph data structures, and the role, importance and applications of artificial neural networks in the development of artificial intelligence software.
Learning Outcomes (LO) 1. To know the definition, development, future and the concepts of Intelligent Agent in Artificial Intelligence. 2. To comprehend Classical Logic and First Order Logic structures and their relations with each other. 3. To know Fuzzy Logic concept and its advantages over other logic types and applications of artificial intelligence. 4. To be able to reveal the importance of awareness-raising about information security, applications of cryptology, threats and opportunities in the field of artificial intelligence for cryptography and cryptoanalysis. 5. To comprehend the contribution of Binary Search Trees to artificial intelligence software with the Tree data structure. 6. To know the usage and usage areas of the abilities brought by the graph data structure in today's artificial intelligence software. 7. To be able to describe the location of Artificial Neural Networks in the world of Machine Learning and to comprehend the effects of Artificial Neural Networks and the Deep Learning Approaches in subsequent artificial intelligence applications with their reasons.
Mode of Delivery Distance Learning
Course Outline
Week Topics
1. Week Introduction, Objectives, Goals and Processing of the Course
2. Week Definition, Development and Future of Artificial Intelligence
3. Week Classical Logic and First-Order Logic
4. Week Fuzzy Logic
5. Week Information Security
6. Week Cryptology - Symmetric Cypher
7. Week Cryptology – Asymmetric Cypher
8. Week MID-TERM
9. Week Expert Systems
10. Week Tree Data Structure and Searching Algorithms
11. Week Graphs
12. Week Artificial Neural Network – 1
13. Week Artificial Neural Network – 2
14. Week Summary
Mid-term (%)
Quizes (%)
Homeworks/Term papers (%) 40
Practice (%)
Labs (%)
Projects/Field Work (%)
Seminars/Workshops (%)
Final (%) 60
Total(%) 100
Course Book (s) and/or References 1. “Yapay Zeka İnsan-Bilgisayar Etkileşimi”, V. Vagifoğlu Nabiyev, Seçkin Yayıncılık, 2016. 2. “Artificial Intelligence A Modern Approach”, Stuart J. Russel and Peter Norvig, Pearson, 2009. 3. “Artificial Intelligence 101 Things You Must Know Today About Our Future”, L. Rouhiainen, 2018.
Work Placement(s)
The Relationship between Program Qualifications (PQ) and Course Learning Outcomes (LO)
ÖÇ135  3   54 
ÖÇ255  5555   
ÖÇ355  5555   
ÖÇ453    5  5 
ÖÇ555  5555   
ÖÇ655  5555   
ÖÇ755  55554