Lecture Notes Table of Contents (PDF)
Available lecture notes are listed below.
Lecture files.| Lec # | Topics |
|---|
| 1 | Introduction (PDF 1) (PDF 2) |
| Part I: Estimation |
| 2 | Recursive Least Square (RLS) Algorithms (PDF) |
| 3 | Properties of RLS (PDF) |
| 4 | Random Processes, Active Noise Cancellation (PDF) |
| 5 | Discrete Kalman Filter-1 (PDF) |
| 6 | Discrete Kalman Filter-2 (PDF) |
| 7 | Continuous Kalman Filter (PDF) |
| 8 | Extended Kalman Filter (PDF) |
| Part 2: Representation and Learning |
| 9 | Prediction Modeling of Linear Systems (PDF) |
| 10 | Model Structure of Linear Time-invariant Systems (PDF) |
| 11 | Time Series Data Compression, Laguerre Series Expansion (PDF) |
| 12 | Non-linear Models, Function Approximation Theory, Radial Basis Functions (PDF) |
| 13 | Neural Networks (PDF) |
| 14 | Error Back Propagation Algorithm (PDF) |
| Part 3: System Identification |
| 15 | Perspective of System Identification, Frequency Domain Analysis (PDF) |
| 16 | Informative Data Sets and Consistency (PDF) |
| 17 | Informative Experiments: Persistent Excitation (PDF) |
| 18 | Asymptotic Distribution of Parameter Estimates (PDF) |
| 19 | Experiment Design, Pseudo Random Binary Signals (PRBS) (PDF) |
| 20 | Maximum Likelihood Estimate, Cramer-Rao Lower Bound and Best Unbiased Estimate (PDF) |
| 21 | Information Theory of System Identification: Kullback-Leibler Information Distance, Akaike's Information Criterion (PDF) |