Machine Learning for Physics and Astronomy Data Analysis

3 credits

Fundamental principles and applications of data-centric research techniques in Physics and Astronomy. Topics include algorithms for data structuring, dimensionality reduction, linear regression and classification, artificial neural nets, convolutional neural nets, unsupervised learning. [1-4-0] Prerequisite: One of MATH 152, MATH 221, MATH 223 and one of MATH 200, MATH 217, MATH 226, MATH 253, MATH 254 and one of BIOL 300, STAT 200, STAT 203, STAT 241, STAT 251, MATH 318 and one of PHYS 210, CPSC 103, CPSC 110, APSC 160, EOSC 211 and one of PHYS 108, PHYS 118, PHYS 158, SCIE 001.