NOC:Statistical Signal Processing (USB)

₹950.00
In stock
SKU
108103158



Media Storage Type : 32 GB USB Stick

NPTEL Subject Matter Expert : Prof. Prabin Kumar Bora

NPTEL Co-ordinating Institute : IIT Guwahati

NPTEL Lecture Count : 39

NPTEL Course Size : 16 GB

NPTEL PDF Text Transcription : Available and Included

NPTEL Subtitle Transcription : Available and Included (SRT)


Lecture Titles:

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

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