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5 Open Computer Vision Courses Across Top Universities

From Geometric Vision to Deep Learning: Unveiling the Leading-edge Curricula of NYU, UC Berkeley, UW, UT Austin, and Stanford

  1. Computer Vision, New York University

    Explore geometric vision and the impact of convolutional networks on recognition, segmentation, and more. Suitable for students with knowledge of linear algebra and basic machine learning. โ†’ Read more

  2. Visual Object and Activity Recognition, University of California, Berkeley

    Focus on object/activity recognition and deep learning techniques. Reviews recent literature and techniques for real-time applications in robotics and multimedia. โ†’ Read more

  3. Computer Vision, University of Washington

    Covers feature detection, image segmentation, motion estimation, 3D shape reconstruction, and object recognition. Includes topics like image processing, motion estimation, light physics, and 3D modeling. โ†’ Read more

  4. Visual Recognition, University of Texas at Austin

    A discussion-based course analyzing papers on visual recognition, auto-annotation, and scene understanding. Involves paper reviews, class discussions, programming assignments, presentations, and a final project. โ†’ Read more

  5. Deep Learning for Computer Vision, Stanford University

    A comprehensive course on deep learning architectures in computer vision. Covers image classification, localization, and detection with hands-on assignments. โ†’ Read more

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