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Statistical Signal Processing

Track :

Engineering

Lessons no : 38

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What will you learn in this course?
  • Develop skills in analyzing signals using statistical methods and probabilistic models for real-world applications
  • Apply filtering techniques to enhance signal quality and reduce noise in various signal processing scenarios
  • Implement statistical algorithms for signal detection, estimation, and classification in noisy environments
  • Utilize advanced statistical tools to extract meaningful information from complex signals and datasets
  • Design and evaluate signal processing systems using statistical signal processing techniques
  • Interpret statistical signal models to improve accuracy in signal analysis and decision-making
  • Employ machine learning methods integrated with statistical signal processing for predictive analytics
  • Optimize filtering and estimation processes to improve signal clarity and system performance
  • Analyze time-series data to identify patterns, trends, and anomalies in signals
  • Apply Bayesian and non-Bayesian approaches for signal estimation and hypothesis testing
  • Use statistical signal processing techniques to develop robust communication and sensor systems
  • Integrate statistical methods with digital signal processing for enhanced signal analysis and processing

How to Get The Certificate

  • You must have an account Register
  • Watch All Lessons
  • Watch at least 50% of Lesson Duration
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  • You can Register With Any Course For Free
  • The Certificate is free !
Lessons | 38
Lec 1 Overview of Statistical Signal Processing
00:24:21

Lec 1 Overview of Statistical Signal Processing
Lec 2 Probability and Random Variables
00:48:51

Lec 2 Probability and Random Variables
Lec 3 Linear Algebra of Random Variables
00:37:58

Lec 3 Linear Algebra of Random Variables
Lec 4 Random Processes
00:37:18

Lec 4 Random Processes
Lec 5 Linear Shift Invariant Systems with Random Inputs
00:32:28

Lec 5 Linear Shift Invariant Systems with Random Inputs
Lec 6 White Noise and Spectral Factorization Theorem
00:32:51

Lec 6 White Noise and Spectral Factorization Theorem
Lec 7 Linear Models of Random Signals 1
00:37:59

Lec 7 Linear Models of Random Signals 1
Lec 8 Linear Models of Random Signals 2
00:37:36

Lec 8 Linear Models of Random Signals 2
Lec 9 Estimation Theory 1
00:32:53

Lec 9 Estimation Theory 1
Lec 10 Estimation Theory 2 MVUE and Cramer Rao Lower Bound I
00:38:17

Lec 10 Estimation Theory 2 MVUE and Cramer Rao Lower Bound I
Lec 11 Estimation Theory 3 Cramer Rao Lower Bound II
00:26:55

Lec 11 Estimation Theory 3 Cramer Rao Lower Bound II
Lec 12 MVUE through Sufficient Statistic 1
00:30:27

Lec 12 MVUE through Sufficient Statistic 1
Lec 13 MVUE through Sufficient Statistic 2
00:26:15

Lec 13 MVUE through Sufficient Statistic 2
Lec 14 Method of Moments and Maximum Likelihood Estimators
00:29:18

Lec 14 Method of Moments and Maximum Likelihood Estimators
Lec 15 Properties of Maximum Likelihood Estimator MLE
00:25:32

Lec 15 Properties of Maximum Likelihood Estimator MLE
Lec 16 Bayesian Estimators 1
00:31:28

Lec 16 Bayesian Estimators 1
Lec 17 Bayesian Estimators 2
00:26:02

Lec 17 Bayesian Estimators 2
Lec 18 Optimal linear filters Wiener Filter
00:30:03

Lec 18 Optimal linear filters Wiener Filter
Lec 19 Review Assignment 1
00:43:31

Lec 19 Review Assignment 1
Lec 20 FIR Wiener filter
00:23:01

Lec 20 FIR Wiener filter
Lec 21 Non Causual IIR Wiener Filter
00:31:16

Lec 21 Non Causual IIR Wiener Filter
Lec 22 Causal IIR Wiener Filter
00:30:37

Lec 22 Causal IIR Wiener Filter
Lec 23 Review Assignment 2
00:45:49

Lec 23 Review Assignment 2
Lec 24 Linear Prediction of Signals 1
00:27:57

Lec 24 Linear Prediction of Signals 1
Lec 25 Linear Prediction of Signals 2
00:29:52

Lec 25 Linear Prediction of Signals 2
Lec 26 Linear Prediction of Signals 3
00:26:29

Lec 26 Linear Prediction of Signals 3
Lec 27 Solution to Review Assignment 3
00:39:32

Lec 27 Solution to Review Assignment 3
Lec 28 Solution to Review Assignment 4
00:47:26

Lec 28 Solution to Review Assignment 4
Lec 29 Adaptive Filters 1
00:33:12

Lec 29 Adaptive Filters 1
Lec 30 Adaptive Filters 2 LMS algorithm
00:26:09

Lec 30 Adaptive Filters 2 LMS algorithm
Lec 31 Adaptive Filters 3 Convergence of the LMS algorithm
00:30:13

Lec 31 Adaptive Filters 3 Convergence of the LMS algorithm
Lec 32 Adaptive Filters 4 Variants of the LMS algorithm
00:31:31

Lec 32 Adaptive Filters 4 Variants of the LMS algorithm
Lec 33 Least squares Estimator
00:30:30

Lec 33 Least squares Estimator
Lec 34 Properties of Least squares Estimator
00:43:53

Lec 34 Properties of Least squares Estimator
Lec 35 Recursive Least Squares RLS Adaptive Filter 2
00:30:39

Lec 35 Recursive Least Squares RLS Adaptive Filter 2
Lec 36 Kalman Filter 1
01:11:21

Lec 36 Kalman Filter 1
Lec 37 Vector Kalman Filter
00:49:36

Lec 37 Vector Kalman Filter
Lec 38 Review
00:38:58

Lec 38 Review


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