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Explore comprehensive computer vision concepts through this university-level course covering fundamental image processing techniques and modern deep learning approaches. Begin with essential mathematical foundations including linear algebra basics before progressing through core computer vision topics such as image filtering, edge detection, and feature extraction. Master neural network fundamentals and dive deep into convolutional neural networks, learning both theoretical concepts and practical implementation through hands-on PyTorch tutorials. Advance through sophisticated computer vision applications including image classification techniques, object detection methods, and various segmentation approaches including semantic and instance segmentation. Conclude with specialized topics in motion analysis through optical flow, action recognition systems, and camera modeling principles. Each lecture builds systematically on previous concepts, providing both theoretical understanding and practical skills needed for computer vision applications in research and industry settings.
