Modules / Lectures
Module NameDownloadDescriptionDownload Size
Module 1 : Motivation and Overview, Probability and Statistics Review 1Probability and Statistics Review (Part 1)Probability and Statistics Review (Part 1)1200 kb
Module 1 : Motivation and Overview, Probability and Statistics Review 1Introduction, Motivation & OverviewIntroduction, Motivation & Overview1800 kb
Module 2 : Probability and Statistics Review 2, Introduction to Random ProcessesProbability and Statistics Review (Part 2)Probability and Statistics Review (Part 2)560 kb
Module 2 : Probability and Statistics Review 2, Introduction to Random ProcessesIntroduction to Random ProcessesIntroduction to Random Processes316 kb
Module 3 : Introduction to Random Processes, Autocovariance and Autocorrelation FunctionsIntroduction to Random Processes (contd.)Introduction to Random Processes (contd.)340 kb
Module 3 : Introduction to Random Processes, Autocovariance and Autocorrelation FunctionsAutocovariance & Autocorrelation FunctionsAutocovariance & Autocorrelation Functions770 kb
Module 4 : Autocovariance and Autocorrelation Functions, Partial Autocorrelation Functions, Models fPartial Autocorrelation FunctionsPartial Autocorrelation Functions640 kb
Module 4 : Autocovariance and Autocorrelation Functions, Partial Autocorrelation Functions, Models fAutocovariance & Autocorrelation Functions (contd.)Autocovariance & Autocorrelation Functions (contd.)340 kb
Module 4 : Autocovariance and Autocorrelation Functions, Partial Autocorrelation Functions, Models fModels for Linear Stationary ProcessesModels for Linear Stationary Processes530 kb
Module 5 : Models for Linear Stationary Processes, Models for Linear Non Stationary ProcessesModels for Linear Stationary ProcessesModels for Linear Stationary Processes(contd)Models for Linear Stationary Processes(contd)470 kb
Module 5 : Models for Linear Stationary Processes, Models for Linear Non Stationary ProcessesModels for Linear Non-stationary ProcessesModels for Linear Non-stationary Processes(contd)Models for Linear Non-stationary Processes(contd)320 kb
Module 6 : Models for Linear Non Stationary Processes, Fourier Transforms for Deterministic SignalsModels for Linear Non-stationary Processes (contd.)Models for Linear Non-stationary Processes (contd.)650 kb
Module 6 : Models for Linear Non Stationary Processes, Fourier Transforms for Deterministic SignalsFourier Transforms for Deterministic SignalsFourier Transforms for Deterministic Signals980 kb
Module 7 : Fourier Transforms for Deterministic Signals, DFT and Periodogram, Spectral RepresentatiSpectral Representations of Random ProcessesSpectral Representations of Random Processes757 kb
Module 7 : Fourier Transforms for Deterministic Signals, DFT and Periodogram, Spectral RepresentatiFourier Transforms, DFT and PeriodogramFourier Transforms, DFT and Periodogram1700 kb
Module 8 : Spectral Representations of Random Processes, Introduction to Estimation Theory, GoodnessIntroduction to Estimation TheoryIntroduction to Estimation Theory757 kb
Module 8 : Spectral Representations of Random Processes, Introduction to Estimation Theory, GoodnessGoodness of EstimatorsGoodness of Estimators470 kb
Module 8 : Spectral Representations of Random Processes, Introduction to Estimation Theory, GoodnessSpectral Representations of Random Processes(contd)Spectral Representations of Random Processes(contd)700 kb
Module 9 : Goodness of Estimators 2, Estimation Methods 1Properties of EstimatorsProperties of Estimators1500 kb
Module 9 : Goodness of Estimators 2, Estimation Methods 1Estimation methodsEstimation methods780 kb
Module 10 : Estimation Methods 1, Estimation Methods 2, MLE and Bayesian EstimationMLE and Bayesian EstimationMLE and Bayesian Estimation600 kb
Module 10 : Estimation Methods 1, Estimation Methods 2, MLE and Bayesian EstimationEstimation Methods (MoM, OLS, WLS, NLS)Estimation Methods (MoM, OLS, WLS, NLS)780 kb
Module 11 : MLE and Bayesian Estimation, Estimation of Time Domain Properties, Estimation of Power SCase study 2Case study 2340 kb
Module 11 : MLE and Bayesian Estimation, Estimation of Time Domain Properties, Estimation of Power SPeriodogram as PSD EstimatorPeriodogram as PSD Estimator580 kb
Module 11 : MLE and Bayesian Estimation, Estimation of Time Domain Properties, Estimation of Power SCase study 1Case study 1475 kb
Module 11 : MLE and Bayesian Estimation, Estimation of Time Domain Properties, Estimation of Power SEstimation of Time-Series ModelsEstimation of Time-Series Models700 kb
Module 11 : MLE and Bayesian Estimation, Estimation of Time Domain Properties, Estimation of Power SEstimation of Power Spectral DensityEstimation of Power Spectral Density1300 kb
Module 11 : MLE and Bayesian Estimation, Estimation of Time Domain Properties, Estimation of Power SEstimation of Time Domain StatisticsEstimation of Time Domain Statistics550 kb
Module 11 : MLE and Bayesian Estimation, Estimation of Time Domain Properties, Estimation of Power SMLE and Bayesian Estimation (contd.)MLE and Bayesian Estimation (contd.)588 kb

Sl.No Chapter Name English
1Lecture01_Part1 - Motivation and Overview 1Download
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2Lecture01_Part2 - Motivation and Overview 2Download
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3Lecture02_Part1 - Motivation and Overview 3Download
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4Lecture02_Part2 - Motivation and Overview 4Download
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5Lecture03_Part1 - Motivation and Overview 5Download
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6Lecture03_Part2 - Motivation and Overview 6Download
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7Lecture04_Part1 - Probability and Statistics Review 1ADownload
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8Lecture04_Part2 - Probability and Statistics Review 1BDownload
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9Lecture05_Part1 - Probability and Statistics Review 1CDownload
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10Lecture05_Part2 - Probability and Statistics Review 1DDownload
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11Lecture06_Part1 - Probability and Statistics Review 2ADownload
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12Lecture06_Part2 - Probability and Statistics Review 2BDownload
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13Lecture06_Part3 - Probability and Statistics Review 2CDownload
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14Lecture07_Part1 - Probability and Statistics Review 2DDownload
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15Lecture07_Part2 - Probability and Statistics Review 2EDownload
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16Lecture07_Part3 - Probability and Statistics Review 2FDownload
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17Lecture08_Part1 - Probability and Statistics Review 2G (with R Demonstration)Download
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18Lecture08_Part2 - Probability and Statistics Review 2H (with R Demonstration)Download
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19Lecture9_Part1 - Probability and Statistics Review 2 IDownload
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20Lecture9_Part2 - Probability and Statistics Review 2JDownload
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21Lecture9_Part3 - Introduction to Random Processes 1Download
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22Lecture10_Part1 - Introduction to Random Processes 2Download
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23Lecture10_Part2 - Introduction to Random Processes 3Download
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24Lecture11_Part1 - Introduction to Random Processes 4Download
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25Lecture11_Part2 - Introduction to Random Processes 5Download
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26Lecture11_Part3 - Autocovariance & Autocorrelation Functions 1Download
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27Lecture12_Part1 - Autocovariance & Autocorrelation Functions 2Download
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28Lecture12_Part2 - Autocovariance & Autocorrelation Functions 3Download
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29Lecture13_Part1 - Autocovariance & Autocorrelation Functions 4Download
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30Lecture13_Part2 - Autocovariance & Autocorrelation Functions 5Download
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31Lecture13_Part3 - Autocovariance & Autocorrelation Functions 6Download
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32Lecture14_Part1 - Autocovariance & Autocorrelation Functions 7Download
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33Lecture14_Part2 - Autocovariance & Autocorrelation Functions 8Download
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34Lecture15_Part1 - Autocovariance & Autocorrelation Functions 9Download
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35Lecture15_Part2 - Partial Autocorrelation FunctionsDownload
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36Lecture16_Part1 - Autocorrelation and Partial-autocorrelation Functions (with R Demonstration)Download
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37Lecture16_Part2 - Models for Linear Stationary Processes 1Download
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38Lecture17_Part1 - Models for Linear Stationary Processes 2Download
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39Lecture17_Part2 - Models for Linear Stationary Processes 3Download
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40Lecture18_Part1 - Models for Linear Stationary Processes 4Download
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41Lecture18_Part2 - Models for Linear Stationary Processes 5Download
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42Lecture18_Part3 - Models for Linear Stationary Processes 6Download
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43Lecture19_Part1 - Models for Linear Stationary Processes 7Download
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44Lecture19_Part2 - Models for Linear Stationary Processes 8Download
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45Lecture19_Part3 - Models for Linear Stationary Processes 9Download
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46Lecture20_Part1 - Models for Linear Stationary Processes 10Download
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47Lecture20_Part2 - Models for Linear Stationary Processes 11Download
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48Lecture21_Part1 - Models for Linear Stationary Processes 12Download
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49Lecture21_Part2 - Models for Linear Stationary Processes 13Download
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50Lecture22_Part1 - Models for Linear Stationary Processes 14 (with R Demonstrations)Download
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51Lecture22_Part2 - Models for Linear Stationary Processes 15 (with R Demonstrations)Download
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52Lecture22_Part3 - Models for Linear Stationary Processes 16 (with R Demonstrations)Download
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53Lecture23_Part1 - Models for Linear Non-stationary Processes 1Download
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54Lecture23_Part2 - Models for Linear Non-stationary Processes 2 (with R Demonstrations)Download
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55Lecture24_Part1 - Models for Linear Non-stationary Processes 3 (with R Demonstrations)Download
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56Lecture24_Part2 - Models for Linear Non-stationary Processes 4Download
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57Lecture25_Part1 - Models for Linear Non-stationary Processes 5Download
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58Lecture25_Part2 - Models for Linear Non-stationary Processes 6 (with R Demonstrations)Download
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59Lecture26_Part1 - Fourier Transforms for Deterministic Signals 1Download
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60Lecture26_Part2 - Fourier Transforms for Deterministic Signals 2Download
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61Lecture27_Part1 - Fourier Transforms for Deterministic Signals 3Download
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62Lecture27_Part2 - Fourier Transforms for Deterministic Signals 4Download
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63Lecture28_Part1 - Fourier Transforms for Deterministic Signals 5Download
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64Lecture28_Part2 - Fourier Transforms for Deterministic Signals 6Download
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65Lecture29_Part1 - Fourier Transforms for Deterministic Signals 7Download
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66Lecture29_Part2 - Fourier Transforms for Deterministic Signals 8Download
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67Lecture30_Part1 - Fourier Transforms for Deterministic Signals 9Download
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68Lecture30_Part2 - DFT and Periodogram 1Download
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69Lecture31_Part1 - DFT and Periodogram 2Download
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70Lecture31_Part2 - DFT and Periodogram 3 (with R Demonstrations)Download
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71Lecture32_Part1 - Spectral Representations of Random Processes 1Download
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72Lecture32_Part2 - Spectral Representations of Random Processes 2Download
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73Lecture33_Part1 - Spectral Representations of Random Processes 3Download
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74Lecture33_Part2 - Spectral Representations of Random Processes 4Download
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75Lecture33_Part3 - Spectral Representations of Random Processes 5Download
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76Lecture34_Part1 - Spectral Representations of Random Processes 6Download
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77Lecture34_Part2 - Spectral Representations of Random Processes 7Download
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78Lecture35_Part1 - Introduction to Estimation Theory 1Download
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79Lecture35_Part2 - Introduction to Estimation Theory 2Download
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80Lecture35_Part3 - Introduction to Estimation Theory 3Download
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81Lecture 36A - Introduction to Estimation Theory -4Download
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82Lecture 36B - Goodness of Estimators 1 -1Download
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83Lecture 37A - Goodness of Estimators 1 -2Download
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84Lecture 37B - Goodness of Estimators 1 -3Download
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85Lecture 37C - Goodness of Estimators 1 -4Download
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86Lecture 38A - Goodness of Estimators 2 -1Download
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87Lecture 38B - Goodness of Estimators 2 -2Download
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88Lecture 38C - Goodness of Estimators 2 -3Download
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89Lecture 39A - Goodness of Estimators 2 -4Download
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90Lecture 39B - Goodness of Estimators 2 -5 (with R demonstrations)Download
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91Lecture 39C - Goodness of Estimators 2 -6Download
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92Lecture 40A - Goodness of Estimators 2 -7Download
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93Lecture 40B - Goodness of Estimators 2 -8Download
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94Lecture 41A - Estimation Methods 1 -1Download
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95Lecture 41B - Estimation Methods 1 -2Download
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96Lecture 42A - Estimation Methods 1 -3Download
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97Lecture 42B - Estimation Methods 1 -4Download
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98Lecture 42C - Estimation Methods 1 -5Download
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99Lecture 43A - Estimation Methods 1 -6 (with R demonstrations)Download
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100Lecture 43B - Estimation Methods 1 -7(with R demonstrations)Download
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101Lecture 44A - Estimation Methods 1 -8Download
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102Lecture 44B - Estimation Methods 1 -9Download
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103Lecture 44C - Estimation Methods 2 -1Download
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104Lecture 45A - Estimation Methods 2 -2Download
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105Lecture 45B - Estimation Methods 2 -3Download
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106Lecture 46A - MLE and Bayesian Estimation -1Download
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107Lecture 46B - MLE and Bayesian Estimation -2Download
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108Lecture 47A - MLE and Bayesian Estimation -3Download
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109Lecture 47B - MLE and Bayesian Estimation -4Download
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110Lecture 48A - Estimation of Time Domain Statistics -1Download
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111Lecture 48B - Estimation of Time Domain Statistics -2Download
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112Lecture 49 - Periodogram as PSD EstimatorDownload
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113Live SessionPDF unavailable


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