Prerequisites: CS 225 or EE 231 or EE 236 or EE 244 or CS 171 or EE 142; graduate standing; or consent of instructor
Description: Explores fundamentals of deep neural networks and their applications in various machine learning tasks. Includes the fundamentals of perception, approximation, neural network architectures, loss functions, and generalization. Addresses optimization methods including backpropagation, automatic differentiation, and regularization. Covers non- standard problems including auto-encoders and probabilistic models. Presents applications in machine learning/computer vision.
Cross-listing: Cross-listed with CS 228.
Credit: May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor.