TÜRKÇE
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Information
| Code | YZ008 |
| Name | Computer Vision |
| Term | 2024-2025 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 |
Dr. Öğr. Üyesi SERKAN KARTAL
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
The aim of this course is to teach students the methodologies used in the field of computer vision and to enable students to explore the latest techniques and applications used in this field.
Course Content
To learn computer vision methods, convolutional neural networks, deep learning methods
Course Precondition
Python programming knowledge
Resources
Richard Szeliski, Computer Vision: Algorithms and Application, Springer, 2011, 9783030343712
Notes
Richard Szeliski, Computer Vision: Algorithms and Application, Springer, 2011, 9783030343712
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Having the necessary knowledge to address complex problems in the field of computer vision |
| LO02 | Having the ability to formulate problems in the field of computer vision |
| LO03 | Having the necessary skills for problem solving with algorithms in the field of computer vision |
| LO04 | Having knowledge about algorithms in computer vision |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Beceriler - Bilişsel, Uygulamalı | To be able to access information broadly and deeply by conducting scientific research in the field, to be able to evaluate, interpret and apply the information. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Has a comprehensive knowledge of current techniques and methods applied in engineering and their limitations. | 4 |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | To be able to use uncertain, limited or incomplete data to complete and apply knowledge using scientific methods; to be able to use knowledge from different disciplines together. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Is aware of new and emerging practices of the profession, examines and learns them when needed. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Defines and formulates problems related to the field, develops methods to solve them and applies innovative methods in solutions. | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Develops new and/or original ideas and methods; designs complex systems or processes and develops innovative/alternative solutions in their designs. | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Designs and implements theoretical, experimental and modeling-based research; examines and solves complex problems encountered in this process. | 4 |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | To be able to work effectively in disciplinary and multidisciplinary teams, to lead such teams and to develop solution approaches in complex situations; to be able to work independently and take responsibility. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | To be able to communicate orally and in writing in a foreign language at least at the B2 level of the European Language Portfolio. | |
| PLO10 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | To be able to communicate the process and results of his/her studies systematically and clearly in written or oral form in national and international environments in or outside the field. | |
| PLO11 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Knows the social, environmental, health, safety, legal, project management and business life practices of engineering applications and is aware of the constraints these impose on engineering applications. | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in the stages of data collection, interpretation and announcement and in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to computer vision | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 2 | Image Classification | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 3 | Failure Functions and Optimization | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 4 | Neural Networks and Backpropagation | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 5 | Convolutional Neural Networks | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 6 | Deep Learning | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 7 | Training Neural Networks | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Testing and Evaluation of Neural Networks | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 10 | CNN Architectures | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 11 | Object Detection | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 12 | Image Segmentation | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 13 | Commonly used libraries | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 14 | Project presentations 1 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 15 | Project presentations 2 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Preparation for the exam | 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 | 1 | 15 | 15 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 20 | 20 |
| Total Workload (Hour) | 162 | ||
| Total Workload / 25 (h) | 6,48 | ||
| ECTS | 6 ECTS | ||