Spring 2024
Meeting:
W 1:30pm - 4:20pm / PAB B143
SLN:
19038
Section Type:
Laboratory
Catalog Description:
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.
Credits:
Status:
Active
Last updated:
June 20, 2025 - 7:18 am