EECS 225A: Statistical Signal Processing

Lectures

• Lecture 1 (Reviewing -transform and DTFT)

• Lecture 2 (Linear Estimation)

• Lecture 3 (Joint Gaussian Distribution and WSS Stochastic Processes)

• Lecture 4 (Spectral Representation of Random Processes)

• Lecture 5 (Noncausal Wiener Filter)

• Lecture 6 (Causal Wiener Filter)

• Lecture 7 (Wiener Filter for Vectors and the Prediction Problem)

• Lecture 8 (Wold Decomposition and the Kolmogorov–Szego Formula)

• Lecture 9 (State Estimation for Hidden Markov Processes)

• Lecture 10 and 11 (State Estimation for Hidden Markov Processes II)

• Lecture 12 (Innovations Process)

• Lecture 13 (State Space Models)

• Lecture 14 (Wide-Sense Markovity and Kalman Filter)

• Lecture 15 (Recursive Least Squares Algorithm)

• Lecture 16 (Gradient Descent and Least Mean Squares Algorithm)

• Lecture 17 (Stochastic Gradient Methods)

• Lecture 18 ( and Estimation Theory)

• Lecture 19 (Schmidt's Modifications to the Kalman Filter)

• Lecture 20 (-Optimality of the LMS and Risk-Sensitive Estimation)

• Lecture 21 (Kalman Smoother)

• Lecture 22 (Hamiltonian Equations for Kalman Smoother)

• Lecture 23 (Triangularization and Array Algorithms for Kalman Filter)

• Lecture 24 (CKMS Recursion)

• Lecture 25 (Duality in Linear Estimation Theory)

• Lecture 26 (Adaptivity of Stochastic Gradient Methods)