CS 478 - Introduction to Deep Learning

Units: 3

Introduces use or application of software to cover fundamental topics of classification, regression and clustering, and a number of corresponding learning models such as the multi-layer perceptron and its gradient-based training through the backpropagation algorithm. Fully connected neural networks will be followed by more specialized neural network architectures such as convolutional neural networks (for images), recurrent neural networks (for sequences), and memory-augmented neural networks. Recommended Preparation: MATH 374 or MATH 264; MATH 260; and MATH 342.

Prerequisite(s): CS 311 and MATH 242 with grades of C (2.0) or better.