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 460 | Course Introduction and Application Information
| Course Name |
Intelligent Systems
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
MCE 460
|
Fall/Spring
|
3
|
0
|
3
|
6
|
| 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 give Mechatronics engineering students with basic knowledge and ability to employ intelligent systems. Students will learn how to employ neural networks, fuzzy logic, and other nature inspired algorithms. By examining the case studies, they will gain experience about applications in real engineering problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning Outcomes |
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Course Description | Introduction to intelligent systems and nature inspired algorithms. Review for Optimization, modeling and control. Introduction to neural networks, back propagation learning rule, fuzzy set theory, fuzzy inference methods, fuzzy control, adaptive neuro-fuzzy inferencing system (ANFIS), genetic algorithms. Case studies with applications | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
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 Intelligent Systems | Textbook 1: Chapter 1 | |
| 2 | Introduction to Artificial Intelligence | Textbook 1: Chapter 1 | |
| 3 | Perceptron learning rule | Textbook 1: Chapter 2 | |
| 4 | Backpropagation learning rule | Textbook 1: Chapter 2 | |
| 5 | Design and validation of a neural networks | Textbook 1: Chapter 6 | |
| 6 | Use of neural networks for modeling and control | Textbook 1: Chapter 6 | |
| 7 | Midterm Exam | ||
| 8 | Introduction to fuzzy logic, fuzzy set theory | Textbook 1: Chapter 4 | |
| 9 | Fuzzy composition and inferencing | Textbook 1: Chapter 4 | |
| 10 | Fuzzy control | Textbook 1: Chapter 4 | |
| 11 | Adaptive neuro-fuzzy inferencing system | Textbook 1: Chapter 8 | |
| 12 | Different combinations of neural networks and fuzzy systems | Textbook 1: Chapter 8 | |
| 13 | Particle Swarm optimization and Genetic algorithm | Textbook 1: Chapter 7 | |
| 14 | Project Presentations | ||
| 15 | Review of Semester | ||
| 16 | Final Exam |
| Course Notes/Textbooks | Artificial Intelligence: A Guide to Intelligent Systems, Michael Negnevitsky , Second- Edition 2005, Addison-Wesley , ISBN 0 321 20466 2 |
| Suggested Readings/Materials | 1. Artificial Intelligence: A Modern Approach, Contributing writers: Ernest Davis, Douglas D. Edwards, David Forsyth, Nicholas J. Hay, Jitendra M. Malik, Vibhu Mittal, Mehran Sahami, Sebastian Thrun, Third Edition 2010, Editors: Stuart J. Russell and Peter Norvig, PRENTICE HALL. |
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 |
1
|
10
|
||||||
| Presentation / Jury |
1
|
10
|
||||||
| Project |
1
|
10
|
||||||
| Seminar / Workshop | ||||||||
| Oral Exams | ||||||||
| Midterm |
1
|
30
|
||||||
| Final Exam |
1
|
40
|
||||||
| Total |
| Weighting of Semester Activities on the Final Grade |
4
|
60
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
40
|
| 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 |
1
|
10
|
10
|
| Presentation / Jury |
1
|
10
|
10
|
| Project |
1
|
20
|
20
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
15
|
15
|
| Final Exam |
1
|
25
|
25
|
| Total |
176
|
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 |
-
|
X
|
-
|
-
|
-
|
|
| 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. |
-
|
-
|
X
|
-
|
-
|
|
| 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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