Neural Network Methods for Signals in Engineering and Physical Sciences

3 credits

Practical introduction to neural networks and their applications in the analysis of signal data common in engineering and physical sciences. Students build computational skills for training neural networks, understand and work with modern algorithms, and complete projects developing neural net models to solve data analysis problems from the frontier of sciences. Prerequisite: either CSE 160, STAT 180, E E 241, ASTR 300, or AMATH 301; recommended: PHYS 434; and working knowledge of Python.