NOC:Statistical Signal Processing


Lecture 1 - Overview of Statistical Signal Processing


Lecture 2 - Probability and Random Variables


Lecture 3 - Linear Algebra of Random Variables


Lecture 4 - Random Processes


Lecture 5 - Linear Shift Invariant Systems with Random Inputs


Lecture 6 - White Noise and Spectral Factorization Theorem


Lecture 7 - Linear Models of Random Signals


Lecture 8 - Estimation Theory - 1


Lecture 9 - Estimation Theory - 2: MVUE and Cramer Rao Lower Bound


Lecture 10 - Cramer Rao Lower Bound 2


Lecture 11 - MVUE through Sufficient Statistic - 1


Lecture 12 - MVUE through Sufficient Statistic - 2


Lecture 13 - Method of Moments and Maximum Likelihood Estimators


Lecture 14 - Properties of Maximum Likelihood Estimator (MLE)


Lecture 15 - Bayesian Estimators - 1


Lecture 16 - Bayesian Estimators - 2


Lecture 17 - Optimal linear filters: Wiener Filter


Lecture 18 - FIR Wiener filter


Lecture 19 - Non-Causual IIR Wiener Filter


Lecture 20 - Causal IIR Wiener Filter


Lecture 21 - Linear Prediction of Signals - 1


Lecture 22 - Linear Prediction of Signals - 2


Lecture 23 - Linear Prediction of Signals - 3


Lecture 24 - Review Assignment - 1


Lecture 25 - Adaptive Filters - 1


Lecture 26 - Adaptive Filters - 2


Lecture 27 - Adaptive Filters - 3


Lecture 28 - Review Assignment - 2


Lecture 29 - Adaptive Filters - 4


Lecture 30 - Adaptive Filters - 4 (Continued...)


Lecture 31 - Review Assignment - 3


Lecture 32 - Recursive Least Squares (RLS) Adaptive Filter - 1


Lecture 33 - Recursive Least Squares (RLS) Adaptive Filter - 2


Lecture 34 - Review Assignment - 4


Lecture 35 - Kalman Filter - 1


Lecture 36 - Vector Kalman Filter


Lecture 37 - Linear Models of Random Signals


Lecture 38 - Review - 1


Lecture 39 - Review - 2