| Course Title | Code | Language | Type | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| Statistics and Optimization | MEM387 | Turkish | Compulsory | 5. Semester | 2 + 0 | 2.0 | 3.0 |
| Prerequisite Courses | |
| Course Level | Undergraduate |
| Mode of delivery | Face to face |
| Course Coordinator | Prof. Dr. Gürcan SAMTAŞ |
| Instructor(s) | |
| Goals | • Apply statistical methods for analyzing and interpreting sensor data in mechatronic systems. • Utilize probability theory, statistical distributions, and regression analysis for modeling experimental and sensor-based data. • Handle uncertainty and noise in control systems through error estimation and statistical decision-making. • Introduce optimization concepts and apply them for system design, control, and performance enhancement. • Integrate statistical thinking and optimization techniques for data-driven modeling and engineering problem-solving in mechatronics. |
| Course Content | This course provides fundamental and advanced concepts in statistics and optimization that are essential for analyzing, modeling, and improving mechatronic systems. The first part (Weeks 1–10) focuses on statistical tools such as data analysis, probability, statistical distributions, regression, and hypothesis testing used in sensor data interpretation and model validation. The second part (Weeks 11–14) introduces optimization techniques including linear, nonlinear, and multi-objective methods, as well as modern optimization algorithms applied to mechatronic system design and control. Students will learn how to integrate statistical analysis with optimization approaches to enhance system performance and decision-making in engineering applications. |
| # | Öğrenme Kazanımı |
| 1 | Analyze and interpret sensor data using appropriate statistical tools. |
| 2 | Mekatronik sistemlerde belirsizlikleri ve hataları modellemek için olasılık dağılımlarını kullanır. |
| 3 | Apply regression analysis to predict system behavior and validate models. |
| 4 | Conduct hypothesis testing and interpret statistical relationships between system variables. |
| 5 | Implement statistical error estimation and decision-making methods in control systems. |
| 6 | Formulate and solve optimization problems using linear and nonlinear optimization techniques. |
| 7 | Apply modern optimization algorithms such as genetic algorithms and particle swarm optimization to engineering problems. |
| 8 | Integrate statistical analysis and optimization methods to evaluate and improve mechatronic system performance. |
| Week | Topics/Applications | Method |
|---|---|---|
| 1. Week | Introduction: Statistics, Data Types, and Characteristics of Sensor Data | Research, Preparation, After Class Study |
| 2. Week | Probability Concepts and Random Variables | Preparation, After Class Study, Research |
| 3. Week | Probability Distributions and Their Applications in Mechatronic Systems | Preparation, After Class Study, Research |
| 4. Week | Descriptive Statistics and Data Visualization | Preparation, After Class Study, Research |
| 5. Week | Sampling, Estimation, and Confidence Intervals | Research, Preparation, After Class Study |
| 6. Week | Hypothesis Testing and Engineering Applications | Research, Preparation, After Class Study |
| 7. Week | Linear Programming & Design Optimization | Preparation, After Class Study, Research |
| 8. Week | Linear Regression and Applications in Mechatronic Systems | Research, Preparation, After Class Study |
| 9. Week | Multiple Regression and Model Validation | Preparation, After Class Study, Research |
| 10. Week | Noise Analysis, Error Estimation, and Decision-Making under Uncertainty | Preparation, After Class Study, Research |
| 11. Week | Introduction to Optimization and Linear Programming | Preparation, After Class Study, Research |
| 12. Week | Nonlinear and Constrained Optimization Methods | Research, Preparation, After Class Study |
| 13. Week | Multi-Objective Optimization and Engineering Design Applications | Preparation, After Class Study, Research |
| 14. Week | Optimization Algorithms (Genetic Algorithms, Particle Swarm Optimization) and Mechatronic Applications | Research, Preparation, After Class Study |
| 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 | ✔ | |||||
| 4 | To effectively use up-to-date software and hardware tools with an awareness of project management, risk management, entrepreneurship, innovation, and sustainable development | ✔ | |||||
| 5 | To acquire professional responsibility and ethical awareness | ✔ | |||||
| 6 | To gain the ability to work both individually and as part of a team | ✔ | |||||
| 7 | To gain the ability to communicate effectively in oral and written form, and to use a foreign language efficiently in professional life | ✔ | |||||
| 8 | To gain awareness of the necessity of lifelong learning, the ability to access information, to follow scientific and technological developments, to continuously improve oneself, and to recognize the health, environmental, safety, and legal aspects of engineering practices | ✔ | |||||
| Program Requirements | DK1 | DK2 | DK3 | DK4 | DK5 | DK6 | DK7 | DK8 |
|---|---|---|---|---|---|---|---|---|
| PY1 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
| PY2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| PY3 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
| PY4 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| PY5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| PY6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| PY7 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| PY8 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 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 | 3 | 42 |
|
Ders Dışı |
Preparation, After Class Study | 14 | 1 | 14 |
| Research | 14 | 1 | 14 | |
| Other Activities | 1 | 4.5 | 4.5 | |
|
Sınavlar |
Midterm 1 | 1 | 1 | 1 |
| Final | 1 | 1 | 1 | |
| Total Workload | 76.5 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 3.0 | ||
