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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| COMPUTER SCIENCES PR. | |
| Code | BBZ412 |
| Name | Artificial Intelligence Systems |
| Term | 2026-2027 Academic Year |
| Semester | 8. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. YUSUF ALPER KAPLAN |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
To provide information about artificial intelligence, to demonstrate the design and operating principles of artificial intelligence systems, and to model intelligent systems.
Course Content
Problems of Artificial Intelligence. Intelligence Tests. Turing test. Chinese room test. Problem-solving methods. State space. Search techniques. Heuristic analysis. Games. Alpha-beta and min-max algorithms. Data modeling. Semantic networks. Frame model. Scene model. Knowledge base. Rules. Expert systems. Natural language processing. Parsers. Production systems. Computerized recognition. Recognition of handwriting and printed characters. Knowledge base architecture. Learning. Artificial Neural Networks (ANN) and learning. Object recognition using ANN. Analysis and synthesis of voice, voice recognition.
Course Precondition
none
Resources
1 Nabiyev V. V., 2005 Yapay Zeka: Problemler, Yöntemler, Algoritmalar, Ankara (2. Baskı) 2 Russell, Stuart J. ; Norvig, Peter, 2003 , Artificial Intelligence: A Modern Approach (2nd ed. )
Notes
Nilsson, Nils,1998 , Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-55860-467-4
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Compare artificial and natural intelligence and possess knowledge about the fundamental problems of artificial intelligence. |
| LO02 | Decide whether to prioritize basic search or heuristic search techniques in solving various problems. |
| LO03 | Perform knowledge modeling and program it on a computer. |
| LO04 | Understand how basic behavioral patterns such as speech, natural language, and learning are modeled in computer applications; apply basic approaches such as ANN (Artificial Neural Networks) and genetic algorithms. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Gain comprehensive knowledge of fundamental concepts, algorithms, and data structures in Computer Science. | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learn essential computer topics such as software development, programming languages, and database management | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understand advanced computer fields like data science, artificial intelligence, and machine learning. | 5 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Acquire knowledge of topics like computer networks, cybersecurity, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develop skills in designing, implementing, and analyzing algorithms | 4 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gain proficiency in using various programming languages effectively | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learn skills in data analysis, database management, and processing large datasets. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Acquire practical experience through working on software development projects. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthen teamwork and communication skills. | |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | Foster a mindset open to technological innovations. | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourage the capacity for continuous learning and self-improvement. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Enhance the ability to solve complex problems |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Problems of Artificial Intelligence. Intelligence Tests. Turing test. Chinese room test. State space. | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 2 | Problem-solving methods. | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 3 | Search techniques. Heuristic analysis. | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Games. Alpha-beta and min-max algorithms. | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | Knowledge base. Facts and Rules. Creating Knowledge-based Systems. | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 6 | Data modeling. Semantic networks. Frame model. Scene model. | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 7 | Expert systems. Production systems. | Reading lecture notes | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 8 | Mid-Term Exam | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Pattern Recognition. | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Recognition of handwriting and printed characters. | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Biometric recognition. | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 12 | Natural language processing. Parsers. | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 13 | Learning. Artificial Neural Networks applications. | Reading lecture notes | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 14 | Voice analysis and synthesis, voice recognition. | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 15 | Knowledge base architecture. Object recognition via ANN. | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 16 | Term Exams | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 18 | 18 |
| Final Exam | 1 | 18 | 18 |
| Total Workload (Hour) | 120 | ||
| Total Workload / 25 (h) | 4,80 | ||
| ECTS | 5 ECTS | ||