CEN462 Introduction to Computer Vision

6 ECTS - 3-0 Duration (T+A)- 8. Semester- 3 National Credit

Information

Code CEN462
Name Introduction to Computer Vision
Term 2024-2025 Academic Year
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 Instructor
1


Course Goal / Objective

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 Adequate knowledge of mathematics, science and related engineering disciplines; ability to use theoretical and applied knowledge in these fields in solving complex engineering problems. 3
PLO02 Bilgi - Kuramsal, Olgusal Ability to identify, formulate and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. 4
PLO03 Bilgi - Kuramsal, Olgusal Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose. 4
PLO04 Bilgi - Kuramsal, Olgusal Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively. 4
PLO05 Bilgi - Kuramsal, Olgusal Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics. 4
PLO06 Bilgi - Kuramsal, Olgusal Ability to work effectively in interdisciplinary and multidisciplinary teams; individual working skills. 3
PLO07 Bilgi - Kuramsal, Olgusal Ability to communicate effectively verbally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, prepare design and production reports, make effective presentations, and give and receive clear and understandable instructions. 2
PLO08 Bilgi - Kuramsal, Olgusal Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology, and constantly renew oneself.
PLO09 Bilgi - Kuramsal, Olgusal Knowledge of ethical principles, professional and ethical responsibility, and standards used in engineering practice.
PLO10 Bilgi - Kuramsal, Olgusal Knowledge of business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; knowledge of sustainable development.
PLO11 Bilgi - Kuramsal, Olgusal Knowledge of the effects of engineering practices on health, environment and safety in universal and social dimensions and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions.


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

Update Time: 11.05.2024 09:03