CS 547 Deep Learning Neural Networks
Review of basic machine learning models (regression, classification) and numerical optimization; Feedforward networks; Loss functions; Back-propagation training; Regularization; Convolutional neural networks; Recurrent and recursive networks; Vanishing gradient problem; Long-short term memory (LSTM) model; Gated recurrent units (GRUs); Auto-encoders Generative adversarial networks; Applications of shallow and deep neural networks.
Credits
3
Prerequisite
CS 501 or admission to the Artificial Intelligence MS program or Software Engineering MS program. Expectation that student has prerequisite knowledge of discrete math, linear algebra, and statistics; check with the instructor if you have questions about these expectations.