Machine Learning for Signal Processing Applications

4 credits

Application of machine learning and deep learning algorithms to real-world signal, image, and video processing problems using cloud computing with central, graphics, and tensor processing units (CPU/GPU/TPU). Characteristics of multi-dimensional signals and systems. Unsupervised and supervised learning. Deep learning convolutional neural networks. Generative adversarial learning. Open long-tailed recognition. Object detection and segmentation. Course overlaps with: TEE 461. Prerequisite: a minimum grade of 1.0 in E E 242; either MATH 136, both MATH 126 and MATH 208, or both MATH 126 and AMATH 352; and either E E 391, IND E 315, MATH 394/STAT 394, or STAT 390.