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Deep learning (DL) has emerged as a powerful tool for solving data-intensive learning problems such as supervised learning for classification or regression, dimensionality reduction, and control. As such, it has a broad range of applications including language processing, computer vision, medical imaging, and perception-based robotics. The goal of this course is to directly apply fundamental concepts of DL to current research problems. Students will apply theoretical underpinnings of machine learning, commonly used architectures for DL, current challenges including ethics and fairness, and specialized applications with a particular focus on computer vision. Students will complete several DL projects using standardized data sets in addition to a small-team research project on topics of their own interest. Prerequisites: A neural network OR machine learning course: Examples: EN.605.647, EN.625.638, EN.525.670, EN.605.649, EN.705.601, EN.605.646, or others as approved by the instructor. A working knowledge of Python is assumed. Prior coding experience data munging, ML, and visualization libraries is highly recommended: Example: Python, Numpy, Pandas, ScikitLearn, Matplotlib, etc.