Deep learning methods are highly effective at solving many problems in computer vision. This course serves as an introduction to these two areas and covers both the theoretical and practical aspects required to build effective deep learning-based computer vision applications. Topics include classification, convolutional neural networks, object detection, encoder-decoders, segmentation, keypoint and pose estimation, generative adversarial networks, and transformers. Labs and assignments will emphasize practical implementations of deep learning systems applied to computer vision problems.
(Lec: 3, Lab: 0.5, Tut: 0)