Information Package / Course Catalogue
| Artificial Intelligence | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
ECTS Course Information Package
| School/Faculty/Institute | Faculty of Engineering | ||||||||
| Course Code | AI 482 | ||||||||
| Course Title in English | Computer Vision | ||||||||
| Course Title in Turkish | Bilgisayarlı Görü | ||||||||
| Language of Instruction | EN | ||||||||
| Type of Course | Flipped Classroom | ||||||||
| Level of Course | Select | ||||||||
| Semester | Fall | ||||||||
| Contact Hours per Week |
|
||||||||
| Estimated Student Workload | 161 hours per semester | ||||||||
| Number of Credits | 6 ECTS | ||||||||
| Grading Mode | Standard Letter Grade | ||||||||
| Pre-requisites | None | ||||||||
| Co-requisites | None | ||||||||
| Expected Prior Knowledge | Object Oriented Programming, Data Structures | ||||||||
| Registration Restrictions | Only Undergraduate Students | ||||||||
| Overall Educational Objective | To become familiar with the fundamental concepts of Computer Vision, such as image formation, camera parameters, preprocessing, convolution, segmentation, edge and corner detection, line and ellipse fitting, image understanding and object recognition. | ||||||||
| Course Description | This course provides a comprehensive introduction to some fundamental aspects of Computer Vision. The following topics are covered: Introduction, Image formation, camera parameters, preprocessing, convolution, segmentation, edge and corner detection, line and ellipse fitting, Object Tracking, image understanding and object recognition, Deep Learning. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Understand image formation process, camera parameters and projections; 2) Apply convolution for filtering and preprocessing; 3) Apply probability and statistics to solve problems in computer vision; 4) Develop feature extractors such as edge, corner, blob detectors; 5) Develop vision solutions using deep learning methods 6) Develop image understanding and objects recognition solutions; 7) Communicate effectively by means of reports and presentations; 8) Analyze and interpret data, and use engineering judgment to draw conclusions; 9) Acquire and apply new knowledge as needed. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|
| 1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | |||||||||
| 2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | |||||||||
| 3) An ability to communicate effectively with a range of audiences | |||||||||
| 4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | |||||||||
| 5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | |||||||||
| 6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | |||||||||
| 7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | H | HW,Project,Exam |
| 2) | An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | H | Select,HW,Project,Exam |
| 3) | An ability to communicate effectively with a range of audiences | S | Project |
| 4) | An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | N | |
| 5) | An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | N | |
| 6) | An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | S | HW,Project,Exam |
| 7) | An ability to acquire and apply new knowledge as needed, using appropriate learning strategies | S | HW,Project,Exam |
| Prepared by and Date | MUHİTTİN GÖKMEN , February 2026 |
| Course Coordinator | TUBA AYHAN |
| Semester | Fall |
| Name of Instructor |
Course Contents
| Week | Subject |
| 1) | Introduction |
| 2) | Convolution |
| 3) | Filtering |
| 4) | Features: Edge Detection |
| 5) | Features: HoG, Harris Corners, SIFT |
| 6) | Cameras and Image Formation |
| 7) | Neural Networks – Back Propagation, Training |
| 8) | Convolutional Neural Networks (CNN) |
| 9) | Deep Learning - Autoencoders |
| 10) | Deep Learning – Classification and Recognition |
| 11) | Deep Learning - Object Detection |
| 12) | Deep Learning - Segmentation |
| 13) | Deep- Learning - Transformers |
| 14) | Stereo and Optical flow |
| 15) | Final Exam/Project/Presentation Period |
| 16) | Final Exam/Project/Presentation Period |
| Required/Recommended Readings | Computer Vision: Algorithms and Applications, Richard Szeliski, Springer Science & Business Media, 2010 Introductory Techniques for 3-D Computer Vision, by Emanuele Trucco, Alessandro Verri, Prentice-Hall, 1998 | ||||||
| Teaching Methods | Lecturing and exercises in the classroom with computers. In-class exercises and 3 Projects will be carried out by students | ||||||
| Homework and Projects | In-class exercises, 3 Projects | ||||||
| Laboratory Work | Programming exercises | ||||||
| Computer Use | For Programming | ||||||
| Other Activities | |||||||
| Assessment Methods |
|
||||||
| Course Administration |
gokmenm@mef.edu.tr 0 212 395 3626; 5th Floor , #551 Devam kuralları: Minimum %70 devam zorunludur. Quiz kaçırma: Geçerli mazeret belgeleri sunulduğu takdirde, öğrencinin kaçırdığı her quiz, diğer tüm quizlerin ortalamasına eşit bir not ile değerlendirilecektir. Telafi sınavı yapılmayacaktır. Ara sınavı kaçırma: Geçerli mazeret belgeleri sunulduğu takdirde, ara sınav için telafi sınavı sağlanacaktır. Final sınavı kaçırma: Fakülte yönetmelikleri geçerlidir. Uygunsuz davranış, akademik dürüstlük ihlali ve intihal, Yükseköğretim Kanunu’nun 54. maddesine tabidir. |
||||||
ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 1 | 3 | 1 | 70 | ||
| Project | 8 | 5 | 3 | 2 | 80 | ||
| Midterm(s) | 1 | 3 | 1 | 4 | |||
| Final Examination | 1 | 5 | 2 | 7 | |||
| Total Workload | 161 | ||||||
| Total Workload/25 | 6.4 | ||||||
| ECTS | 6 | ||||||