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Deep Learning

STAT 6342

School of Natural Sciences and Mathematics

Deep neural network models as generalizations of traditional shallow learning models; feedforward neural networks; loss and activation functions; optimization for deep neural networks; backpropagation algorithm; regularization methods; methods for improving generalizability; convolutional neural networks; recurrent and other neural network models for sequence data; autoencoders; and deep generative models. Computer packages such as R or Python will be used for the implementation of methods and data analysis. Department consent required. 3 credit hours.

Prerequisite: (STAT 5353 or equivalent) or instructor consent.

Offering Frequency: Every two years

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