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Information
| Code | CENG0038 |
| Name | Computer Vision |
| Term | 2024-2025 Academic Year |
| Semester | . Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator |
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
Basic python programming, statistics, linear algebra
Resources
Computer Vision: Algorithms and Application, Richard Szeliski.
Notes
Deep Learning for Vision Systems, Mohamed Elgendy
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Build computer vision applications. |
| LO02 | Learning the concepts used in object classification. |
| LO03 | Become familiar with widely used computer vision libraries. |
| LO04 | Learning the concepts used in object detection. |
| LO05 | Learning the concepts used for image segmentation. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. | 2 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 5 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 4 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | 4 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | 3 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | 3 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. | 3 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | 2 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to computer vision | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri, Beyin Fırtınası |
| 2 | Image Classification | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 3 | Yitim Fonksiyonları ve Optimizasyon | 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: Anlatım, Gösteri |
| 8 | Mid-Term Exam | 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 I | 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: Gösteri, Anlatım |
| 13 | Widely used libraries | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Gösteri, Anlatım |
| 14 | Preparation of project presentations | Preparation of project presentation | Öğretim Yöntemleri: Tartışma, Soru-Cevap, Proje Temelli Öğrenme |
| 15 | Project presentations. | Reading lecture notes, project presentation. | Öğretim Yöntemleri: Proje Temelli Öğrenme , Tartışma, Soru-Cevap |
| 16 | Term Exams | Preparation of the project report | Ölçme Yöntemleri: Proje / Tasarım |
| 17 | Term Exams | Preparation of the project report | Ölçme Yöntemleri: Proje / Tasarım, Sözlü Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||