Deep Learning: 3 semester hours

Catalog description

This course focuses on the underlying theory, the range of applications to which deep learning has been applied, and learning from very large data sets. Topics include deep feed-forward networks, optimization for training deep models, convolutional and recurrent neural networks, structured probabilistics models, autoencoders, and Monte Carlo methods. The course will also train students to use open-source software such as TensorFlow to gain hands-on experiences.