EMBEDDED ARTIFICIAL INTELLIGENCE and COMPUTER VISION
-
Spring Semester
2+2
3
6
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of Course
English
Course Level
First Cycle (Bachelor's Degree)
Course Type
Elective
Course Coordinator
Prof.Dr. Bahadır Kürşat GÜNTÜRK
Name of Lecturer(s)
Prof.Dr. Bahadır Kürşat GÜNTÜRK
Assistant(s)
Aim
Develop artificial intelligence and computer vision applications in edge devices (Nvidia Jetson)
Course Content
This course contains; Introduction to Linux operating system,Installation of Nvidia Jetson Nano,Face detection application,Installation and use of CSI camera,Utilizing GPU functions of OpenCV,Optical flow and object detection applications,OpenCV DNN module applications,TensorRT model optimization and usage,Mediapipe application,Tesseract application,Nvidia Jetson GPIO usage,Semester project progress (I),Semester project progress (II),Project demo.
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
Develops artificial intelligence and computer vision applications in resource constraint platforms
14
F
Uses Nvidia Jetson platform
14
F
Teaching Methods:
14: Self Study Method
Assessment Methods:
F: Project Task
Course Outline
Order
Subjects
Preliminary Work
1
Introduction to Linux operating system
2
Installation of Nvidia Jetson Nano
3
Face detection application
4
Installation and use of CSI camera
5
Utilizing GPU functions of OpenCV
6
Optical flow and object detection applications
7
OpenCV DNN module applications
8
TensorRT model optimization and usage
9
Mediapipe application
10
Tesseract application
11
Nvidia Jetson GPIO usage
12
Semester project progress (I)
13
Semester project progress (II)
14
Project demo
Resources
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
1. An ability to apply knowledge of mathematics, science, and engineering
2
2. An ability to identify, formulate, and solve engineering problems
X
3
3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
X
4
4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
X
5
5. An ability to design and conduct experiments, as well as to analyze and interpret data
X
6
6. An ability to function on multidisciplinary teams
7
7. An ability to communicate effectively
X
8
8. A recognition of the need for, and an ability to engage in life-long learning
X
9
9. An understanding of professional and ethical responsibility
10
10. A knowledge of contemporary issues
X
11
11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
Assessment Methods
Contribution Level
Absolute Evaluation
Rate of Midterm Exam to Success
30
Rate of Final Exam to Success
70
Total
100
ECTS / Workload Table
Activities
Number of
Duration(Hour)
Total Workload(Hour)
Course Hours
0
0
0
Guided Problem Solving
0
0
0
Resolution of Homework Problems and Submission as a Report
0
0
0
Term Project
0
0
0
Presentation of Project / Seminar
0
0
0
Quiz
0
0
0
Midterm Exam
0
0
0
General Exam
0
0
0
Performance Task, Maintenance Plan
0
0
0
Total Workload(Hour)
0
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(0/30)
0
ECTS of the course: 30 hours of work is counted as 1 ECTS credit.
Detail Informations of the Course
Course Description
Course
Code
Semester
T+P (Hour)
Credit
ECTS
EMBEDDED ARTIFICIAL INTELLIGENCE and COMPUTER VISION
-
Spring Semester
2+2
3
6
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of Course
English
Course Level
First Cycle (Bachelor's Degree)
Course Type
Elective
Course Coordinator
Prof.Dr. Bahadır Kürşat GÜNTÜRK
Name of Lecturer(s)
Prof.Dr. Bahadır Kürşat GÜNTÜRK
Assistant(s)
Aim
Develop artificial intelligence and computer vision applications in edge devices (Nvidia Jetson)
Course Content
This course contains; Introduction to Linux operating system,Installation of Nvidia Jetson Nano,Face detection application,Installation and use of CSI camera,Utilizing GPU functions of OpenCV,Optical flow and object detection applications,OpenCV DNN module applications,TensorRT model optimization and usage,Mediapipe application,Tesseract application,Nvidia Jetson GPIO usage,Semester project progress (I),Semester project progress (II),Project demo.
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
Develops artificial intelligence and computer vision applications in resource constraint platforms
14
F
Uses Nvidia Jetson platform
14
F
Teaching Methods:
14: Self Study Method
Assessment Methods:
F: Project Task
Course Outline
Order
Subjects
Preliminary Work
1
Introduction to Linux operating system
2
Installation of Nvidia Jetson Nano
3
Face detection application
4
Installation and use of CSI camera
5
Utilizing GPU functions of OpenCV
6
Optical flow and object detection applications
7
OpenCV DNN module applications
8
TensorRT model optimization and usage
9
Mediapipe application
10
Tesseract application
11
Nvidia Jetson GPIO usage
12
Semester project progress (I)
13
Semester project progress (II)
14
Project demo
Resources
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
1. An ability to apply knowledge of mathematics, science, and engineering
2
2. An ability to identify, formulate, and solve engineering problems
X
3
3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
X
4
4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
X
5
5. An ability to design and conduct experiments, as well as to analyze and interpret data
X
6
6. An ability to function on multidisciplinary teams
7
7. An ability to communicate effectively
X
8
8. A recognition of the need for, and an ability to engage in life-long learning
X
9
9. An understanding of professional and ethical responsibility
10
10. A knowledge of contemporary issues
X
11
11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
