Rapor Tarihi: 27.03.2026 05:32
| Course Title | Code | Language | Type | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| Artificial Intelligence Systems | MEM388 | Turkish | Compulsory | 6. Semester | 3 + 0 | 3.0 | 5.0 |
| Prerequisite Courses | |
| Course Level | Undergraduate |
| Mode of delivery | Face to face |
| Course Coordinator | Dr. Öğr. Üyesi GÖKAY ÇORUHLU |
| Instructor(s) | |
| Goals | The aim of this course is to provide mechatronics engineering students with comprehensive knowledge of the fundamental concepts and subfields of artificial intelligence. Topics including search algorithms, constraint satisfaction problems, the Prolog programming language, machine learning, linear and logistic regression, artificial neural networks, and deep learning are addressed in the context of both theoretical foundations and mechatronic applications. |
| Course Content | Definition and scope of artificial intelligence; historical development of AI; types and categories of AI; state space search algorithms (BFS, DFS, UCS); heuristic search and the A* algorithm; constraint satisfaction problems (CSP); Prolog programming language and action-based planning; introduction to machine learning and ML types; linear regression; logistic regression and classification; clustering methods (unsupervised learning); artificial neural networks (ANN) — biological/artificial neuron comparison, activation functions, backpropagation; deep neural networks, CNN and RNN architectures; transfer learning and mechatronic applications. |
| # | Öğrenme Kazanımı |
| 1 | Apply state space search algorithms (BFS, DFS, UCS, A) to mechatronic problems.* |
| 2 | Understand constraint satisfaction problems and their solution methods |
| 3 | Explain types and core concepts of machine learning; mathematically formulate, train, and evaluate linear/logistic regression models; solve unsupervised learning problems using K-means clustering. |
| 4 | Understand the fundamental components of artificial neural networks (layers, activation functions) and the backpropagation algorithm, applying them to mechatronic applications; evaluate deep neural network architectures. |
| Week | Topics/Applications | Method |
|---|---|---|
| 1. Week | Definition and Scope of Artificial Intelligence: AI types and categories, AI applications in mechatronics engineering, current AI technologies. | Interview |
| 2. Week | State Space Search Algorithms I: Uninformed search strategies, BFS algorithm | Interview |
| 3. Week | State Space Search Algorithms II: Uninformed search strategies, DFS and UCS algorithms | Interview |
| 4. Week | State Space Search Algorithms III: Informed search strategies, concept of heuristic functions | Interview |
| 5. Week | Constraint Satisfaction Problems (CSP) and Introduction to Prolog: Definition of CSP and fundamental concepts, variables, domains, and constraints | Interview |
| 6. Week | Prolog and Action-Based Planning | Interview |
| 7. Week | Introduction to Machine Learning: Definition and application areas of ML, ML system types — Supervised Learning, Unsupervised Learning, Reinforcement Learning | Interview |
| 8. Week | Linear Regression: gradient descent optimization, training process, model evaluation | Interview |
| 9. Week | Logistic Regression and Classification | Interview |
| 10. Week | Clustering (Unsupervised Learning): Concept of unsupervised learning, K-means algorithm | Interview |
| 11. Week | Artificial Neural Networks I — Fundamental Concepts: activation functions, input/hidden/output layers | Interview |
| 12. Week | Artificial Neural Networks II — Training and Applications, Backpropagation algorithm | Interview |
| 13. Week | Artificial Neural Networks III — Mechatronic Applications | Interview |
| 14. Week | Deep Learning — Deep Neural Networks | Interview |
| No | Program Requirements | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | To gain the ability to apply knowledge of mathematics, science, and engineering in the field of Mechatronics Engineering | ✔ | |||||
| 2 | To gain the ability to design and develop an entire mechatronic system or one of its components under realistic constraints and conditions | ✔ | |||||
| 3 | To gain the ability to identify, model, and solve engineering problems | ✔ | |||||
| Program Requirements | DK1 | DK2 | DK3 | DK4 |
|---|---|---|---|---|
| PY1 | 3 | 3 | 3 | 3 |
| PY2 | 3 | 3 | 3 | 3 |
| PY3 | 5 | 5 | 5 | 5 |
| Ders Kitabı veya Notu | Ders Kitabı veya Ders Notu bulunmamaktadır. |
|---|---|
| Diğer Kaynaklar |
|
| ECTS credits and course workload | Quantity | Duration (Hour) | Total Workload (Hour) | |
|---|---|---|---|---|
|
Ders İçi |
Class Hours | 14 | 2 | 28 |
|
Ders Dışı |
Preparation, After Class Study | 10 | 1 | 10 |
|
Sınavlar |
Midterm | 1 | 40 | 40 |
| Final | 1 | 49.5 | 49.5 | |
| Total Workload | 127.5 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 5.0 | ||
