This course builds upon the foundations laid down by MIE1769H to teach many of the core concepts in computer vision with a focus on applications for the "car of the future" and the "factory of the future." Key topics will include: 1) implementation of deep learning architectures (e.g., CNNs, RNNs, Autoencoders, GANs, Transformers, etc.) for image classification, detection, and segmentation, 2) modeling and sensor fusion techniques (e.g., Kalman Filter, Particle Filters, A* Search, etc.) for state estimation, localization and planning, 3) design and deployment of models for real-time operation in the cloud. To ensure that this course is best tailored to the needs of the automotive industry, all concepts will be introduced and demonstrated by using real-life data with a focus on manufacturing and automotive applications. Basic working knowledge of Python and common scientific packages (e.g., NumPy, Matplotlib, and PyTorch) is required.