Estimation & System Identification

Course Description

This course covers the theoretical tools and computational algorithms for estimating the state and unknown parameters of a stochastic dynamical system from noisy sensory measurements. Students will develop both mathematical understanding and hands-on implementation skills through problem sets and programming exercises.

Topics span classical methods such as the Kalman filter and its nonlinear extensions, as well as modern approaches including particle filters, optimal transport–based methods, and connections to machine learning.

Logistics

Weekly Schedule