| Teaching Staff
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| Language of Instruction |
Türkçe (Turkish) |
| Type Of Course |
Elective |
| Prerequisites |
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| Recommended Optional Programme Component |
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| Course Objectives |
ARTIFICIAL INTELLIGENCE FUNDAMENTALS |
| 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 |
Classical Logic and First-Order 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 |
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| Assessment |
| |
Percentage(%) |
| Mid-term (%) |
40 |
| Quizes (%) |
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| Homeworks/Term papers (%) |
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| Practice (%) |
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| Labs (%) |
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| Projects/Field Work (%) |
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| Seminars/Workshops (%) |
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| Final (%) |
60 |
| Other (%) |
ÖDEVLER FİNALE %20 ORANINDA ETKİ EDECEKTİR |
| Total(%) |
100 |
|
| Course Book (s) and/or References |
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| Work Placement(s) |
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| The Relationship between Program Qualifications (PQ) and Course Learning Outcomes (LO) |
| | PÇ1 | PÇ2 | PÇ3 | PÇ4 | PÇ5 | PÇ6 | PÇ7 | PÇ8 | PÇ9 | PÇ10 | PÇ11 | | ÖÇ1 | 5 | | 5 | | 2 | 2 | | | | | | | ÖÇ2 | 5 | 5 | 5 | | | | | | | | | | ÖÇ3 | 5 | 5 | 5 | | | | | | | | | | ÖÇ4 | 5 | 5 | 5 | | 4 | 3 | 3 | | 2 | | | | ÖÇ5 | 5 | 5 | 5 | | | | | | | | | | ÖÇ6 | 5 | 5 | 5 | | | | | | | | | | ÖÇ7 | 5 | 5 | 5 | | | | | | | 3 | |
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