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ENGLISH
Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| COMPUTER ENGINEERING (PhD) (ENGLISH) | |
| Code | CENG602 |
| Name | Advanced Topics in Computer Vision |
| Term | 2025-2026 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. SERKAN KARTAL |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The goal of this course is to provide students with a comprehensive understanding of advanced topics in computer vision, enabling them to explore cutting-edge techniques and applications in the field. By the end of the course, students will be equipped with the knowledge and skills necessary to tackle complex challenges in computer vision research and development.
Course Content
This course covers a wide range of advanced topics in computer vision, including recurrent neural networks (RNNs), attention mechanisms, object detection, image segmentation, video understanding, visual interpretation techniques, self-supervised learning, and generative models.
Course Precondition
Prior knowledge of basic concepts in computer vision, including image processing, feature extraction, and machine learning, is required. Proficiency in programming languages such as Python and familiarity with deep learning frameworks like TensorFlow or PyTorch are also prerequisites for this course.
Resources
Richard Szeliski, Computer Vision: Algorithms and Applications
Notes
Deep Learning for Vision Systems, Mohamed Elgendy
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Understand the inner workings of deep learning models in computer vision |
| LO02 | Design and implement cutting-edge algorithms to address complex computer vision challenges. |
| LO03 | Evaluate the performance of advanced computer vision systems |
| LO04 | Learning how to apply advanced computer vision techniques |
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. | 4 |
| 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. | 3 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 4 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 2 |
| 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. | |
| 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. | |
| 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. | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | |
| 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 Advanced Computer Vision | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 2 | Introduction to Recurrent Neural Networks | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 3 | Recurrent Neural Networks (RNNs) | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 4 | Introduction to Attention Mechanisms and Transformers | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 5 | Attention Mechanisms and Transformers | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 6 | Introduction to Object Detection and Image Segmentation | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 7 | Object Detection and Image Segmentation | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Video Understanding | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 10 | Visualizing and Understanding | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 11 | Self-supervised Learning | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 12 | Generative Models | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 13 | Deep Reinforcement Learning | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 14 | Project work and presentations | Reading the lecture notes | Öğretim Yöntemleri: Proje Temelli Öğrenme , Tartışma, Soru-Cevap |
| 15 | Project presentations | Reading the lecture notes | Öğretim Yöntemleri: Proje Temelli Öğrenme , Tartışma, Soru-Cevap |
| 16 | Term Exams | Ölçme Yöntemleri: Proje / Tasarım |
|
| 17 | Term Exams | 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 | 14 | 14 |
| Final Exam | 1 | 28 | 28 |
| Total Workload (Hour) | 154 | ||
| Total Workload / 25 (h) | 6,16 | ||
| ECTS | 6 ECTS | ||