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Recent advances in machine learning, and deep convolutional neural networks in particular, together with modern GPU computing and greater data availability have accelerated the adoption of deep learning in medical imaging. This course introduces the foundations of deep learning, with applications ranging from image formation to analysis, through hands-on assignments and projects in: image denoising and artifact correction; image processing, segmentation, object detection, and classification; single- and multi-modality registration; generative modeling; and sequential learning across major medical imaging modalities. Recommended course background: Python and Linear Algebra