TÜRKÇE
ENGLISH
Information
| Code | CEN462 |
| Name | Introduction to Computer Vision |
| Semester | 8. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi SERKAN KARTAL |
Course Goal
This course is designed to give students the ability to build computer vision applications. The student will learn the major approaches involved in computer vision.
Course Content
In this course, the fundamental principles and sample applications of computer vision will be explained to the students. Throughout the course, a series of basic concepts 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. More importantly, students will be guided in interesting computer vision projects where they can use up-to-date algorithms.
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 | Become familiar with the major technical approaches involved in computer vision. |
| LO03 | Learning the concepts used for object classification. |
| LO04 | Learning the concepts used for image segmentation. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Has capability in the fields of mathematics, science and computer that form the foundations of engineering | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Identifies, formulates, and solves engineering problems, selects and applies appropriate analytical methods and modeling techniques, | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Analyzes a system, its component, or process and designs under realistic constraints to meet the desired requirements,gains the ability to apply the methods of modern design accordingly. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Ability to use modern techniques and tools necessary for engineering practice and information technologies effectively. | 4 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to design and to conduct experiments, to collect data, to analyze and to interpret results | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Has ability to work effectively as an individual and in multi-disciplinary teams, take sresponsibility and builds self-confidence | 3 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Can access information,gains the ability to do resource research and uses information resources | 2 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Awareness of the requirement of lifelong learning, to follow developments in science and technology and continuous self-renewal ability | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Ability to communicate effectively orally and in writing, and to read and understand technical publications in at least one foreign language | |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Professional and ethical responsibility, | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Awareness about project management, workplace practices, employee health, environmental and occupational safety, and the legal implications of engineering applications, | |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | Becomes aware of universal and social effects of engineering solutions and applications, entrepreneurship and innovation, and knowledge of contemporary issues |
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 | 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, part I | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 8 | Mid-Term Exam | Reading material related to subject and lecture notes. | Ölçme Yöntemleri: Yazılı Sınav, Proje / Tasarım |
| 9 | Training Neural Networks, part II | 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 | Recurrent Neural Networks | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 12 | Unsupervised Learning | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Gösteri, Anlatım |
| 13 | Self-supervised Learning | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 14 | Visualizing and Understanding | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 15 | Detection and Segmentation | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 16 | Term Exams | Reading material related to subject and lecture notes. | Öğretim Yöntemleri: Anlatım, Gösteri |
| 17 | Term Exams | Reading material related to subject and lecture notes. | Ölçme Yöntemleri: Yazılı 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 | ||