Digital Signal Processing and Learning

Catalog description

This course covers fundamentals of digital signal and image processing (DSP); and its connections to modern machine learning, using a balanced mix between math and hands-on experiments. The course will teach basic concepts in signals and systems, including convolutions, frequency analysis, sampling, compressed sensing, image segmentation, image registration, neural network-based machine learning, and representation learning. We will use multiple hands-on programming assignments to demonstrate the concepts we cover in class and problem sets that will help students practice with the theory. A background in linear algebra and probability will be critical to follow the theoretical concepts. All coding will be done in Python. We will offer supplementary material to help get started with Python coding and reinforce the background in linear algebra and probability.