EE 519 Deep Learning Theory and Fundamentals

Provides an introduction to the theory and practice of deep learning, with an emphasis on deep neural network-based approaches. Topics covered include theoretical principles of learning, including the VC-dimension and model selection, and how these can be used to guide the design and deployment of neural networks. State-of-the-art approaches to current problems are also covered. Programming experience in a high-level language (Matlab or Python) and familiarity with calculus is required.