University of Iowa 3 credits MATH
Mathematical aspects of machine learning; regression methods and related issues of overfitting, generalization error, cross-validation; matrix methods for dimension reduction; optimization for support vector machines and neural networks, including convex duality and "kernel trick" for support vector machines; training of neural networks using backpropagation and gradients; reliability of optimization methods for nonconvex optimization; approximation properties of neural networks; convolutions for handling sound and image data; game theory for adversarial networks. Prerequisites: (MATH:2850 or MATH:3550) and (MATH:2220 or MATH:2550 or MATH:2700).
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