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Deep Learning Systems in Engineering ENSC 413 (4)

Machine learning basics, generalization theory, training, validation, and testing. Introduction to artificial neural networks: feedforward, convolutional, recurrent networks. Types of layers in deep models. Architectural and memory calculations. Regularization and optimization. Hardware architectures for deep learning. The course culminates in a major project focusing on engineering applications of deep learning. Prerequisite: MATH 251, ENSC 280, ENSC 351, ENSC 380, all with a minimum grade of B. Students with credit for ENSC 813 may not take this course for further credit.