Data-Driven Sensing

4 credits

Teaches techniques for data-driven reconstruction and control of high-dimensional signals from sparse measurements. Lectures on several other data-driven sensing topics, including optimal sensor placement, active learning, and advanced data denoising and filtering methods. Recommended: two quarters of math-intensive graduate coursework; previous coursework or work experience with linear algebra; differential equations; linear control; and Python; knowledge or coursework in linear model reduction and linear system identification. Offered: A.