CSC 374 - Deep Learning

This course focuses on theoretical foundations and practical applications of deep learning, the subfield of machine learning concerned with large neural networks trained on large data sets. Topics include training models by stochastic gradient descent, implementing various neural network architectures, and choosing network hyperparameters. Application areas include classification, regression, and reinforcement learning problems. Students will implement their own neural networks from scratch and get experience using state-of-the-art deep learning libraries.

Satisfies Mathematics major and minor requirement.
Satisfies Computer Science major requirement.
Satisfies Data Science minor requirement.
Satisfies Applied Math minor requirement.

Prerequisites & Notes
CSC 221 and MAT 150