Rapor Tarihi: 27.03.2026 05:41
| 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 | Arş. Gör. Oğuz EROL |
| 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 | Analyzes sensor data obtained from mechatronic systems using descriptive statistics and probability distributions. |
| 2 | Performs regression analysis to model engineering problems and evaluates the accuracy of the models through hypothesis testing. |
| 3 | Solves problems in engineering design and decision-making processes using appropriate optimization algorithms (linear, non-linear, heuristic, etc.). |
| Week | Topics/Applications | Method |
|---|---|---|
| 1. Week | Introduction: Statistics, Data Types, and Characteristics of Sensor Data | Lecture |
| 2. Week | Probability Concepts and Random Variables | Lecture, Question and Answer |
| 3. Week | Probability Distributions and Their Applications in Mechatronic Systems | Presentation (Preparation), Lecture, Question and Answer |
| 4. Week | Descriptive Statistics and Data Visualization | Lecture |
| 5. Week | Sampling, Estimation, and Confidence Intervals | Presentation (Preparation), Lecture |
| 6. Week | Hypothesis Testing and Engineering Applications | Lecture |
| 7. Week | Linear Programming & Design Optimization | Lecture, Question and Answer |
| 8. Week | Linear Regression and Applications in Mechatronic Systems | Practice, Lecture |
| 9. Week | Multiple Regression and Model Validation | Lecture, Question and Answer |
| 10. Week | Noise Analysis, Error Estimation, and Decision-Making under Uncertainty | Presentation (Preparation), Lecture |
| 11. Week | Introduction to Optimization and Linear Programming | Presentation (Preparation), Lecture, Question and Answer |
| 12. Week | Nonlinear and Constrained Optimization Methods | Practice, Lecture, Question and Answer |
| 13. Week | Multi-Objective Optimization and Engineering Design Applications | Lecture |
| 14. Week | Optimization Algorithms (Genetic Algorithms, Particle Swarm Optimization) and Mechatronic Applications | Practice, Lecture, Question and Answer |
| 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 | ✔ | |||||
| 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 | ✔ | |||||
| Program Requirements | DK1 | DK2 | DK3 |
|---|---|---|---|
| PY1 | 5 | 5 | 5 |
| PY3 | 3 | 3 | 3 |
| PY4 | 3 | 3 | 3 |
| 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 |
| Final | 1 | 1 | 1 | |
| Total Workload | 76.5 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 3.0 | ||
