Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
8BLMS411System Modeling and Simulation Techniques3+0+03510.11.2025

 
Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Computer Engineering
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The aim is to provide students with fundamental knowledge and skills in model-based design and artificial intelligence, to teach requirement management processes, to develop competence in control system design and AI applications, to instruct model verification and testing processes, and to offer knowledge and experience in embedded software development and AI integration. Ultimately, the course seeks to expose students to industrial applications and advanced technologies, enabling them to develop practical, application-oriented projects.
Course Content Over the course of 14 weeks, the program covers model-based design and artificial intelligence in detail. The initial weeks focus on the fundamentals of requirement management and system-level design, followed by in-depth exploration of system modeling techniques, AI fundamentals, and control system design. In the later weeks, emphasis is placed on practical applications such as model verification, testing processes, embedded software development, and AI integration. Throughout this process, students learn how to use modeling tools to integrate AI techniques for solving real-world engineering problems.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator Asist Prof.
Name of Lecturers Dr. SEBAHATTİN BABUR
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources “Model-Based Design for Embedded Systems”, (Gabriela Nicolescu, Pieter J. Mosterman, 2010)
Course Notes Recommended reference books include “Model-Based Design for Embedded Systems” (Gabriela Nicolescu, Pieter J. Mosterman, 2010) and “Deep Learning” (Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 2016). In addition, online MATLAB/Simulink documentation, academic articles, and video lectures can support students as supplementary materials and resources.

Course Category
Mathematics and Basic Sciences %30
Engineering %30
Engineering Design %10
Field %30

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 20
Assignment 1 % 25
Project 1 % 15
Final examination 1 % 40
Total
4
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 2 28
Assignments 8 2 16
Mid-terms 1 10 10
Project 1 10 10
Final examination 1 15 15
Total Work Load   Number of ECTS Credits 5 121

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Students will be able to analyze and design all stages of complex systems, from requirements to system-level models, by applying their engineering and mathematical knowledge and effectively using technical tools such as MATLAB/Simulink.
2 Students will be able to understand the fundamentals of artificial intelligence and machine learning, analyze and optimize data-driven models, and apply appropriate software and analytical tools in developing these models.
3 Students will be able to model and solve problems using classical control theory and engineering principles, while integrating AI-based approaches and technical tools to create advanced, adaptive control solutions.
4 Students will be able to analyze and evaluate the accuracy of system models using engineering fundamentals, employing static analysis, model-in-the-loop, and functional testing tools to assess the reliability of embedded software.
5 Students will be able to apply model-based embedded software development processes, analyze security vulnerabilities, effectively use technical verification and testing tools, and design secure systems with an awareness of ethical and professional responsibility.
6 Students will be able to follow advanced technology trends, plan and manage AI-assisted projects, utilize appropriate technical tools and project management methods, and effectively present their outcomes in professional environments.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Course Introduction & System Modeling
2 Course Introduction & System Modeling
3 Requirements Management
4 Requirements Management
5 System-Level Design
6 Modeling Techniques (MATLAB/Simulink)
7 Modeling Techniques (MATLAB/Simulink)
8 Midterm exam
9 Fundamentals of Artificial Intelligence
10 Control Systems Design
11 AI-Assisted Control
12 Static Model Verification
13 Static Model Verification
14 Model-in-the-Loop & Functional Testing
15 Model-in-the-Loop & Functional Testing
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
All 4 4 4 4
C1 5 4 4
C2 4 5 3
C3 4 4 4
C4 3 3 5
C5 3 3 3
C6 2 2 4

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  https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=243260&curProgID=5607&lang=en