Information Package / Course Catalogue
| Course Code | : MME546 |
| Course Type | : Area Elective |
| Couse Group | : Second Cycle (Master's Degree) |
| Education Language | : English |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 8 |
Students will understand the concepts, theory and computational algorithms needed for several real world recognition or classification from Image data, leading to automated scene understanding or summarization and decision making. Provides necessary skills for understanding of similar tasks on text, speech, video and other forms of data. Students can develop several learning tasks, in several domains ranging from medical, engineering to state of the art industrial and societal needs.
Image processing is one of the most exciting fields in machine learning. It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more. In this course, students will understand image processing and Artificial Intelligence and its various applications.
| 1. | Students will be able to apply the definitions of the image classification and analysis problem to common problems in computer vision |
| 2. | Students will be able to explain the basics of object recognition and image search, object detection techniques, motion estimation, object tracking in video using convolutional filters |
| 3. | Students will be able to apply convolutional neural networks to image data for object recognition and detection |
| 4. | Students will be able to select different network architectures for the appropriate image processing problems |
| 5. | Students will be able to explain the theoretical background of convolutional neural networks in terms of learning rates and system size |
| 1. | R. O. Duda, P. E. Hart , Pattern Classification, WILEY, (2001) |
| 2. | I. Goodfellow, Y. Bengio , Deep Learning, MIT Press, (2016) |
| 3. | Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2010 |
| 4. | Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, G. Aubert and P. Kornprobst, 2nd Edition, Springer-Verlag, 2006 |
| 5. | Markov Random Fields for Vision and Image Processing, Andrew Blake, Pushmeet Kohli, Carsten Rother, The MIT Press, 2011 |
| Type of Assessment | Count | Percent |
|---|---|---|
| Midterm Examination | 1 | %30 |
| Final Examination | 1 | %70 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 3 | 4 | 98 |
| Assignment | 7 | 0 | 5 | 35 |
| Individual Work | 7 | 3 | 3 | 42 |
| Midterm Examination | 1 | 9 | 2 | 11 |
| Final Examination | 1 | 12 | 2 | 14 |
| TOTAL WORKLOAD (hours) | 200 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | PÇ-12 | |
OÇ-1 | 3 | 5 | 5 | 4 | 4 | 4 | 4 | 5 | ||||
OÇ-2 | 3 | 5 | 5 | 3 | 4 | 4 | 4 | 4 | ||||
OÇ-3 | 3 | 5 | 5 | 3 | 4 | 4 | 4 | 4 | ||||
OÇ-4 | 3 | 5 | 5 | 4 | 4 | 4 | 4 | 5 | ||||
OÇ-5 | 3 | 5 | 5 | 4 | 4 | 4 | 4 | 3 | 4 | |||