Lecture, four hours; discussion, two hours; outside study, six hours. Recommended requisites: courses 102, 131A, Mathematics 33A. Covers foundations of computer vision from both theoretical and practical perspective. Particular emphasis on classical computer vision, which should be seen as complementary to deep learning. Study is relevant for various majors in the sciences specializing in artificial intelligence, cyberphysical systems and information engineering, robotics, machine learning, perception, and others looking for applications. Letter grading.