Lectures in this course:41
1 - Introduction to Adaptive Filters (53:56)
2 - Introduction to Stochastic Processes (59:46)
3 - Stochastic Processes (59:46)
4 - Correlation Structure (57:04)
5 - FIR Wiener Filter (Real) (57:42)
6 - Steepest Descent Technique (55:27)
7 - LMS Algorithm (55:03)
8 - Convergence Analysis (59:25)
9 - Convergence Analysis (Mean Square) (54:41)
10 - Convergence Analysis (Mean Square) (53:24)
11 - Misadjustment and Excess MSE (56:13)
12 - Misadjustment and Excess MSE (56:47)
13 - Sign LMS Algorithm (54:45)
14 - Block LMS Algorithm (54:17)
15 - Fast Implementation of Block LMS Algorithm (1:00:49)
16 - Fast Implementation of Block LMS Algorithm (54:43)
17 - Vector Space Treatment to Random Variables (51:48)
18 - Vector Space Treatment to Random Variables (1:00:14)
19 - Orthogonalization and Orthogonal Projection (55:48)
20 - Orthogonal Decomposition of Signal Subspaces (54:59)
21 - Introduction to Linear Prediction (58:30)
22 - Lattice Filter (54:16)
23 - Lattice Recursions (55:41)
24 - Lattice as Optimal Filter (52:57)
25 - Linear Prediction and Autoregressive Modeling (55:57)
26 - Gradient Adaptive Lattice (51:51)
27 - Gradient Adaptive Lattice (56:20)
28 - Introduction to Recursive Least Squares (55:21)
29 - RLS Approach to Adaptive Filters (56:14)
30 - RLS Adaptive Lattice (1:01:43)
31 - RLS Lattice Recursions (54:40)
32 - RLS Lattice Recursions (55:16)
33 - RLS Lattice Algorithm (56:02)
34 - RLS Using QR Decomposition (59:52)
35 - Givens Rotation (1:00:16)
36 - Givens Rotation and QR Decomposition (59:48)
37 - Systolic Implementation (56:34)
38 - Systolic Implementation (48:52)
39 - Singular Value Decomposition (57:01)
40 - Singular Value Decomposition (55:49)
41 - Singular Value Decomposition (59:44)

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