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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 |
| Label | E Elective |
| 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. | |
| 2 | Image Classification | Reading material related to subject and lecture notes. | |
| 3 | Loss Functions and Optimization | Reading material related to subject and lecture notes. | |
| 4 | Neural Networks and Backpropagation | Reading material related to subject and lecture notes. | |
| 5 | Convolutional Neural Networks | Reading material related to subject and lecture notes. | |
| 6 | Deep Learning | Reading material related to subject and lecture notes. | |
| 7 | Training Neural Networks, part I | Reading material related to subject and lecture notes. | |
| 8 | Mid-Term Exam | Reading material related to subject and lecture notes. | |
| 9 | Training Neural Networks, part II | Reading material related to subject and lecture notes. | |
| 10 | CNN Architectures I | Reading material related to subject and lecture notes. | |
| 11 | Recurrent Neural Networks | Reading material related to subject and lecture notes. | |
| 12 | Unsupervised Learning | Reading material related to subject and lecture notes. | |
| 13 | Self-supervised Learning | Reading material related to subject and lecture notes. | |
| 14 | Visualizing and Understanding | Reading material related to subject and lecture notes. | |
| 15 | Detection and Segmentation | Reading material related to subject and lecture notes. | |
| 16 | Term Exams | Reading material related to subject and lecture notes. | |
| 17 | Term Exams | Reading material related to subject and lecture notes. |
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 | ||