University of Delaware 3 credits ELEG
Deep neural networks trained with data-driven optimization are able to act on multimodal data, including text, images, videos, and signals on graphs or networks, in order to model complex relationships, extract information-rich representations, or generate a desired output or distribution in a variety of conditions. Beyond solving tasks such as regression, classification, and compression, deep learning has enabled recent advances in generative artificial intelligence. This course covers the underlying formulations, architectures, and optimization problems involved in deep learning and generative models, blending theoretical perspectives, regarding optimal transport between probability distributions and the compositionality of functions, with practical considerations, including AI ethics and energy requirements for extensive computation. Prerequisite: ELEG 405 or ELEG 605 or CISC 484 or CISC 684 or ELEG 815.
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