— Optimal & Adaptive Filtering

Prerequisites & course information

Course Description: Geometric formulation of least-squares estimation problems. Theory and applications of optimum Wiener and Kalman filtering. MAP and maximum likelihood estimation of hidden Markov models, Viterbi algorithm. Adaptive filtering algorithms, properties and applications. Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Grade Mode: Letter. Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Grade Mode: Letter.