Provides a comprehensive and rigorous introduction to computer vision principles and their application in the Development, Test & Evaluation, and Verification & Validation (DTEVV) of autonomous systems. Students will explore the entire computer vision pipeline, from the physics of image formation to the implementation and validation of state-of-the-art deep learning models. The course is grounded in the theoretical foundations of machine learning and deep learning, while also emphasizing the practical challenges of building robust and reliable systems. A significant portion of the course will be dedicated to hands-on labs and a final project where students will develop, train, and rigorously evaluate a computer vision model for an object tracking task, simulating real-world DTEVV scenarios by analyzing performance against both nominal and disturbed data.