Machine Learning in Geosciences

4 credits

Provides introduction to machine learning in geosciences, basics of computing, and methodologies in applied machine learning. Focuses on canonical and topical data sets in seismology, oceanography, cryosphere, planetary sciences, geology, and geodesy. Topics include unsupervised clustering, logistic regression, random forest, support vector machine, and deep learning. Prerequisite: either CSE 160, CS163, STAT 220, STAT 221/SOC 221/CS&SS 221, STAT 311, STAT 390, MATH 207, MATH 208, AMATH 301, AMATH 351, or AMATH 352. ; recommended: either knowledge in Matlab or Python, AMATH 301, or 100- or 200-level ESS, ATM S, or OCEAN courses in earth sciences. Offered: A.