Statistical Signal Processing

Engineering

2024-01-19

Lessons List

Lec 1 Overview of Statistical Signal Processing

Lec 2 Probability and Random Variables

Lec 3 Linear Algebra of Random Variables

Lec 4 Random Processes

Lec 5 Linear Shift Invariant Systems with Random Inputs

Lec 6 White Noise and Spectral Factorization Theorem

Lec 7 Linear Models of Random Signals 1

Lec 8 Linear Models of Random Signals 2

Lec 9 Estimation Theory 1

Lec 10 Estimation Theory 2 MVUE and Cramer Rao Lower Bound I

Lec 11 Estimation Theory 3 Cramer Rao Lower Bound II

Lec 12 MVUE through Sufficient Statistic 1

Lec 13 MVUE through Sufficient Statistic 2

Lec 14 Method of Moments and Maximum Likelihood Estimators

Lec 15 Properties of Maximum Likelihood Estimator MLE

Lec 16 Bayesian Estimators 1

Lec 17 Bayesian Estimators 2

Lec 18 Optimal linear filters Wiener Filter

Lec 19 Review Assignment 1

Lec 20 FIR Wiener filter

Lec 21 Non Causual IIR Wiener Filter

Lec 22 Causal IIR Wiener Filter

Lec 23 Review Assignment 2

Lec 24 Linear Prediction of Signals 1

Lec 25 Linear Prediction of Signals 2

Lec 26 Linear Prediction of Signals 3

Lec 27 Solution to Review Assignment 3

Lec 28 Solution to Review Assignment 4

Lec 29 Adaptive Filters 1

Lec 30 Adaptive Filters 2 LMS algorithm

Lec 31 Adaptive Filters 3 Convergence of the LMS algorithm

Lec 32 Adaptive Filters 4 Variants of the LMS algorithm

Lec 33 Least squares Estimator

Lec 34 Properties of Least squares Estimator

Lec 35 Recursive Least Squares RLS Adaptive Filter 2

Lec 36 Kalman Filter 1

Lec 37 Vector Kalman Filter

Lec 38 Review

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Akula Sunil

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2025-11-27

Siddhartha

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2025-11-27

Pusala vyshnavi

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2025-11-25

Bommagani sathvik

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2025-11-25

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Statistical Signal Processing course, in this course Explore techniques for analyzing, filtering, and extracting information from signals using statistical methods.

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Statistical Signal Processing

Engineering

Lec 1 Overview of Statistical Signal Processing

Lec 2 Probability and Random Variables

Lec 3 Linear Algebra of Random Variables

Lec 4 Random Processes

Lec 5 Linear Shift Invariant Systems with Random Inputs

Lec 6 White Noise and Spectral Factorization Theorem

Lec 7 Linear Models of Random Signals 1

Lec 8 Linear Models of Random Signals 2

Lec 9 Estimation Theory 1

Lec 10 Estimation Theory 2 MVUE and Cramer Rao Lower Bound I

Lec 11 Estimation Theory 3 Cramer Rao Lower Bound II

Lec 12 MVUE through Sufficient Statistic 1

Lec 13 MVUE through Sufficient Statistic 2

Lec 14 Method of Moments and Maximum Likelihood Estimators

Lec 15 Properties of Maximum Likelihood Estimator MLE

Lec 16 Bayesian Estimators 1

Lec 17 Bayesian Estimators 2

Lec 18 Optimal linear filters Wiener Filter

Lec 19 Review Assignment 1

Lec 20 FIR Wiener filter

Lec 21 Non Causual IIR Wiener Filter

Lec 22 Causal IIR Wiener Filter

Lec 23 Review Assignment 2

Lec 24 Linear Prediction of Signals 1

Lec 25 Linear Prediction of Signals 2

Lec 26 Linear Prediction of Signals 3

Lec 27 Solution to Review Assignment 3

Lec 28 Solution to Review Assignment 4

Lec 29 Adaptive Filters 1

Lec 30 Adaptive Filters 2 LMS algorithm

Lec 31 Adaptive Filters 3 Convergence of the LMS algorithm

Lec 32 Adaptive Filters 4 Variants of the LMS algorithm

Lec 33 Least squares Estimator

Lec 34 Properties of Least squares Estimator

Lec 35 Recursive Least Squares RLS Adaptive Filter 2

Lec 36 Kalman Filter 1

Lec 37 Vector Kalman Filter

Lec 38 Review

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