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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| COMPUTER ENGINEERING (PhD) (ENGLISH) | |
| Code | CENG601 |
| Name | Application of CNN models |
| Term | 2025-2026 Academic Year |
| Term | Fall |
| 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 equip students with a comprehensive understanding of Convolutional Neural Networks (CNNs) and their applications in computer vision tasks. By the end of the course, students will be proficient in designing, implementing, and evaluating CNN models for tasks such as image classification, object detection, semantic segmentation, and beyond.
Course Content
This course covers the theoretical foundations and practical implementations of Convolutional Neural Networks (CNNs), exploring topics such as CNN architectures, image classification, object detection, and semantic segmentation. Through hands-on exercises and projects, students will gain proficiency in designing and deploying CNN models for a variety of computer vision applications.
Course Precondition
The course assumes students have a basic understanding of machine learning concepts, including supervised learning and neural networks, as well as proficiency in programming languages such as Python.
Resources
Python Machine Learning, Sebastian Raschka, 2019, Computer Vision: Algorithms and Application, Richard Szeliski.
Notes
Python Data Science Handbook, Jake VanderPlas, 2017 , Deep Learning for Vision Systems, Mohamed Elgendy
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Comprehensive Understanding of CNN Architectures |
| LO02 | Hands-on Experience with CNN based model Implementation |
| LO03 | Application of CNN models in Real-world Scenarios |
| LO04 | Critical Evaluation and Improvement of CNN Models |
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. | 3 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 3 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | 2 |
| 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. | 2 |
| 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 Convolutional Neural Networks | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 2 | CNN Architectures | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 3 | Image Classification with CNN models | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 4 | Object Detection | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 5 | Object Detection applications with CNN models | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 6 | Semantic Segmentation | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 7 | Semantic Segmentation applications | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Transfer Learning and Fine-tuning | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 10 | Advanced CNN Techniques | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 11 | Convolutional Neural Networks Applications in Industry and Research | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 12 | Review | Reading lecture notes, project presentation. | Öğretim Yöntemleri: Proje Temelli Öğrenme , Tartışma, Soru-Cevap |
| 13 | Project Studies and Demonstrations | Reading lecture notes, project presentation. | Öğretim Yöntemleri: Proje Temelli Öğrenme , Tartışma, Soru-Cevap |
| 14 | Presentations | Reading lecture notes, project presentation. | Öğretim Yöntemleri: Proje Temelli Öğrenme , Tartışma, Soru-Cevap |
| 15 | Project Work and Presentations | Reading lecture notes, project presentation. | Öğretim Yöntemleri: Proje Temelli Öğrenme , Tartışma, Soru-Cevap |
| 16 | Term Exams | Preparation of the project report | Ölçme Yöntemleri: Proje / Tasarım |
| 17 | Term Exams | Preparation of the project report | 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 | ||