This course introduces the theory, algorithms, and practical techniques that enable computers to interpret and reason about visual information. Students will study the full pipeline of modern computer vision, beginning with image formation, camera models, and low-level processing (filtering, edge and feature detection, image pyramids), and progressing through mid-level topics such as feature matching, optical flow, image stitching, segmentation, and stereo and multi-view geometry. The latter portion of the course covers high-level vision tasks including object detection, recognition, and tracking, with an emphasis on both classical methods (Kalman filtering, graph cuts, SIFT/ORB) and contemporary deep learning approaches (CNNs, ResNet and transfer learning, R-CNN family detectors, YOLO, and Vision Transformers).