TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING PR. (ENGLISH) | |
| Code | YZZ210 |
| Name | Artificial Intelligence Systems |
| Term | 2026-2027 Academic Year |
| Semester | 4. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Label | FE Field Education Courses C Compulsory |
| 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
Knowledge representation. Search and intuitive programming. Logic and logic programming. Applications of artificial intelligence: Problem solving, games and puzzles, expert systems, planning, learning, pattern recognition, natural language understanding.
Course Content
Representation of knowledge. Search and heuristic programming. Logic and logic programming. Application areas of artificial intelligence: Problem solving, games and puzzles, expert systems, planning, learning, vision, and natural language understanding. Exercises in an artificial intelligence language
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
1 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 | Learns basic concepts and algorithms of artificial intelligence. |
| LO02 | Learns probabilistic solutions suitable for uncertainties. |
| LO03 | By learning the difference between supervised and unsupervised learning, one can choose the appropriate algorithm for the problem. |
| LO04 | Learns the working principles of artificial neural networks. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | It provides a broad range of knowledge about fundamental Computer Science concepts, algorithms and data structures. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learns basic computer topics such as software development, programming languages, and database management. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understands advanced computing fields such as data science, artificial intelligence, and machine learning. | 5 |
| PLO04 | - | Learn about topics such as computer networks, cyber security, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develops skills in designing, implementing and analyzing algorithms. | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gains the ability to use different programming languages effectively | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learns data analysis, database management and big data processing skills. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Gains practical experience by working on software development projects. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthens collaboration and communication skills within the team. | |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | It provides a mindset open to technological innovations. | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourages continuous learning and self-improvement competence. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Develops the ability to solve complex problems. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Search Problems, Minimax | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 2 | Propositional Logic, Inference | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 3 | Entailment, Model Checking | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 4 | Resolution, First Order Logic | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 5 | Probability, Independence | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 6 | Bayes' Rule, Markov Models | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 7 | Local Search, Simulated Annealing | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Linear Programming, Backtracking Search | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 10 | Data Collecting, Supervised Learning | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 11 | Nearest-Neighbor Classification, Perceptron Learning | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 12 | Support Vector Machines, Regression, Loss Functions | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 13 | Overfitting, Markov Decision Processes, K-Means Clustering | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 14 | Artificial Neural Networks | Reading the lecture notes | Öğretim Yöntemleri: Gösterip Yaptırma, Proje Temelli Öğrenme |
| 15 | Language, Syntax, Transformers | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 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 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 18 | 18 |
| Total Workload (Hour) | 142 | ||
| Total Workload / 25 (h) | 5,68 | ||
| ECTS | 6 ECTS | ||