Lecture Notes

Lecture Notes Table of Contents (PDF)

Available lecture notes are listed below.


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)