Lectures in this course:30
1 - Introduction (43:22)
2 - Probability Theory (46:03)
3 - Random Variables (53:26)
4 - Function of Random Variable Joint Density (55:06)
5 - Mean and Variance (54:11)
6 - Random Vectors Random Processes (48:37)
7 - Random Processes and Linear Systems (46:49)
8 - Some Numerical Problems (54:25)
9 - Miscellaneous Topics on Random Process (51:14)
10 - Linear Signal Models (58:36)
11 - Linear Mean Sq.Error Estimation (54:38)
12 - Auto Correlation and Power Spectrum Estimation (45:22)
13 - Z-Transform Revisited Eigen Vectors/Values (52:08)
14 - The Concept of Innovation (53:33)
15 - Last Squares Estimation Optimal IIR Filters (50:48)
16 - Introduction to Adaptive Filters (47:46)
17 - State Estimation (53:49)
18 - Kalman Filter-Model and Derivation (50:09)
19 - Kalman Filter-Derivation (Contd...) (54:08)
20 - Estimator Properties (49:01)
21 - The Time-Invariant Kalman Filter (52:26)
22 - Kalman Filter-Case Study (53:59)
23 - System identification Introductory Concepts (54:44)
24 - Linear Regression-Recursive Least Squares (49:21)
25 - Variants of LSE (51:36)
26 - Least Square Estimation (51:32)
27 - Model Order Selection Residual Tests (53:00)
28 - Practical Issues in Identification (55:32)
29 - Estimation Problems in Instrumentation and Control (55:25)
30 - Conclusion (55:54)

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