An introductory course on machine vision and related machine learning used in automation, autopilots, security and inspection systems. Topics covered include theory of computer and machine vision and related algorithms for image capture and processing, filtering, thresholds, edge detection, shape analysis, shape detection, salient object detection, pattern matching, digital image stabilization, stereo ranging, and methods of sensor and information fusion. Machine vision sensors covered include visible to long-wave infrared including passive EO/IR (Electro-Optical/Infrared) as well as active methods such as RGB depth mapping and LIDAR. Embedded and automation topics covered include implementation of these algorithms with FPGA or GP-GPU embedded real-time vision systems for autopilots (intelligent transportation), general machine vision automation and security including methods for detection, classification, recognition of targets, and applications including inspection, surveillance, search and rescue, and machine vision navigation.