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 2022-2023 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 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

Update Time: 20.11.2022 02:53