Self-routing autonomous robots by IUE engineers
An autonomous robot that can re-route using artificial intelligence, when it encounters an obstacle, has been developed with the project ...
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Department of Mechatronics Engineering
MCE 390 | Course Introduction and Application Information
| Course Name |
Autonomous and Intelligent Systems
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
MCE 390
|
Fall/Spring
|
3
|
0
|
3
|
5
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
First Cycle
|
|||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | Problem SolvingQ&ALecture / Presentation | |||||
| National Occupation Classification | - | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | This course will provide Mechatronics engineering students with a basic understanding of autonomous systems and the ability to apply intelligent systems. Students will learn how to develop autonomous systems and use neural networks, fuzzy logic, and other nature-inspired algorithms. By examining the case studies, they will gain experience in applying autonomous and intelligent systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning Outcomes |
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Course Description | Introduction to autonomous systems, intelligent systems, and nature-inspired algorithms. Brief review of optimization modeling and control. Introduction to artificial neural networks, backpropagation learning algorithm, fuzzy set theory, fuzzy inference method, fuzzy control, adaptive neural-fuzzy inference rule, genetic algorithm, particle swarm optimization. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Related Sustainable Development Goals |
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation | Learning Outcome |
| 1 | Introduction to Autonomous and Intelligent Systems | Intelligent Systems: Modeling, Optimization, and Control: Chapter 1 | |
| 2 | Introduction to Autonomous and Intelligent Systems | Intelligent Systems: Modeling, Optimization, and Control: Chapter 1 | |
| 3 | Foundations of Autonomy and Autonomous Control | Intelligent Systems: Modeling, Optimization, and Control: Chapter 1 | |
| 4 | Sensing and Perception in Autonomous Systems | Intelligent Systems: Modeling, Optimization, and Control: Chapter 2 | |
| 5 | Autonomy in Decision-Making and Adaptive Control | Intelligent Systems: Modeling, Optimization, and Control: Chapter 2 | |
| 6 | Introduction to Derivative-based and Numerical Optimization | Intelligent Systems: Modeling, Optimization, and Control: Chapter 2 | |
| 7 | Perceptron and Backpropagation Learning Rule | Intelligent Systems: Modeling, Optimization, and Control: Chapter 6 | |
| 8 | Design and Validation of Neural Networks | ||
| 9 | Deep Neural Networks | Intelligent Systems: Modeling, Optimization, and Control: Chapter 2 | |
| 10 | Midterm Exam | Intelligent Systems: Modeling, Optimization, and Control: Chapter 2 | |
| 11 | Classification Algorithms | Intelligent Systems: Modeling, Optimization, and Control: Chapter 7 | |
| 12 | Nature-inspired Algorithms | Intelligent Systems: Modeling, Optimization, and Control: Chapter 2 | |
| 13 | Application Examples | Intelligent Systems: Modeling, Optimization, and Control: Chapter 9 | |
| 14 | Project Presentations | ||
| 15 | Semester Review | ||
| 16 | Final Exam |
| Course Notes/Textbooks | |
| Suggested Readings/Materials |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing | LO 1 | LO 2 | LO 3 | LO 4 | LO 5 | LO 6 |
| Participation | ||||||||
| Laboratory / Application | ||||||||
| Field Work | ||||||||
| Quizzes / Studio Critiques | ||||||||
| Portfolio | ||||||||
| Homework / Assignments |
20
|
|||||||
| Presentation / Jury |
1
|
10
|
||||||
| Project |
1
|
40
|
||||||
| Seminar / Workshop | ||||||||
| Oral Exams | ||||||||
| Midterm |
1
|
30
|
||||||
| Final Exam | ||||||||
| Total |
| Weighting of Semester Activities on the Final Grade |
7
|
100
|
| Weighting of End-of-Semester Activities on the Final Grade | ||
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
16
|
3
|
48
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
4
|
4
|
16
|
| Presentation / Jury |
1
|
10
|
10
|
| Project |
1
|
18
|
18
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
10
|
10
|
| Final Exam |
0
|
||
| Total |
150
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
PC Sub | Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
|||
| 1 |
To have knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems |
-
|
-
|
-
|
-
|
-
|
|
| 2 |
To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose. |
-
|
-
|
X
|
-
|
-
|
|
| 3 |
To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose. |
-
|
X
|
-
|
-
|
-
|
|
| 4 |
To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively. |
-
|
-
|
-
|
X
|
-
|
|
| 5 |
To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems. |
-
|
-
|
-
|
-
|
-
|
|
| 6 |
To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually. |
-
|
-
|
-
|
-
|
-
|
|
| 7 |
To be able to communicate effectively in Turkish, both in oral and written forms; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions. |
-
|
-
|
-
|
-
|
-
|
|
| 8 |
To have knowledge about global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions. |
-
|
-
|
-
|
-
|
-
|
|
| 9 |
To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications. |
-
|
-
|
-
|
-
|
-
|
|
| 10 |
To have knowledge about industrial practices such as project management, risk management and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. |
-
|
-
|
-
|
-
|
-
|
|
| 11 |
Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1) |
-
|
-
|
-
|
-
|
-
|
|
| 12 |
To be able to use the second foreign language at intermediate level. |
-
|
-
|
-
|
-
|
-
|
|
| 13 |
To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering. |
-
|
-
|
-
|
-
|
-
|
|
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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