# Lecture Notes

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)