AI429
ADANCED COMPUTER VISION
- Course Code
- AI429
- Semester
- -
- Theory+Practice
- 3 + 0
- ECTS
- 5
| Course Type | Elective Course |
|---|---|
| Course Level | Bachelor's Degree |
| Year of Study | - |
| Semester | - |
| ECTS | 5 |
| Name of Lecturer(s) | - |
| Learning Outcomes |
At the end of this course, the students; |
| Mode of Delivery | |
| Prerequisites | No |
| Recommended Optional Programme Components | |
| Course Description | This course explores state-of-the-art topics in computer vision including convolutional neural networks, object detection (YOLO, Faster R-CNN), semantic segmentation (U-Net, Mask R-CNN), video analysis, face recognition, and generative adversarial networks (GANs). Students will implement models in PyTorch or TensorFlow. |
| Course Contents | |
| Recommended or Required Reading | * Richard Szeliski, "Computer Vision: Algorithms and Applications", Springer, 2nd ed. * Ian Goodfellow et al., "Deep Learning", MIT Press, 2016. * PyTorch and TensorFlow documentation. |
| Planned Learning Activities and Teaching Methods | |
| Assessment Methods and Criteria | |
| Language of Instruction | - |
| Work Placement(s) | No |