University of Iowa 3 credits ECE
Introduction to deep learning, covering convolutional neural networks, backpropagation, optimization, supervised training of classifiers and regression models, and representation theory; emphasis on designing and implementing algorithms in PyTorch, including practical tips, tricks, and normalization approaches; provides in-depth understanding of machine learning theory and algorithms, with a brief overview of support vector regression and classification, kernel methods, and their connections to deep learning; introduces unsupervised learning methods, including principal component analysis, clustering, autoencoders, and generative adversarial networks. Prerequisites: ECE:2400 or BME:2200.
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