TÜRKÇE
ENGLISH
Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| COMPUTER ENGINEERING (MASTER) (WITHOUT THESIS) | |
| Code | CENGT008 |
| Name | Computer Vision |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
This course is designed to teach students how to develop computer vision applications. The student will learn the algorithms used in computer vision.
Course Content
In this course, the algorithms and sample applications of computer vision will be explained to the students. Throughout the course, a series of libraries related to computer vision will be introduced and their practical application in the projects will be explained. A number of real-world applications that are important to our daily lives will be introduced in general.
Course Precondition
Resources
Computer Vision: Algorithms and Application, Richard Szeliski.
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Build computer vision applications. |
| LO02 | Learning the concepts used in applications such as object classification, object detection, segmentation, etc. |
| LO03 | Become familiar with widely used computer vision libraries. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Belirsiz |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to computer vision | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 2 | Image Classification | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 3 | Loss Functions and Optimization | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 4 | Neural Networks and Backpropagation | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 5 | Convolutional Neural Networks | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 6 | Deep Learning | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 7 | Training Neural Networks | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Gösteri |
| 8 | Mid-Term Exam | Reading material related to subject and lecture notes. | Ölçme Yöntemleri: Yazılı Sınav, Proje / Tasarım |
| 9 | Testing and Evaluation of the Neural Networks | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 10 | CNN Architectures | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 11 | Object Detection | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 12 | Image Segmentation | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 13 | Widely used libraries | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 14 | Application Development | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 15 | Visualizing and Understanding | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 16 | Term Exams | Reading material related to subject and lecture notes. | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Reading material related to subject and lecture notes. | Ölçme Yöntemleri: Yazılı Sınav |