AM160: Introduction to Scientific Machine Learning

This is an introduction to scientific machine learning, where students will learn how to build models of real-life systems from data, e.g., fluid, weather/climate, biological processes, ecology, using machine learning tools seamlessly blended with the theories of ordinary differential equations. This course is in-person and will include in-class programming exercises to gain expertise by practice. The prerequisites of this course are: AM 20 and AM 30; or MATH 24; or PHYS 116A; and AM 129 or CSE 30, or Applied Math graduate students. This course is restricted to juniors and seniors.

5 credits

Year Fall Winter Spring Summer

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