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)

Problem Set 1 (PDF)

ps1files.zip (ZIP) (The ZIP file contains: 13 .txt files.)

Problem Set 2 (PDF)

ps2files.zip (ZIP) (The ZIP file contains: 6 .txt files.)

Problem Set 3 (PDF)

ps3files.zip (ZIP) (The ZIP file contains: 6 .txt files.)

Problem Set 4 (PDF)

Problem Set 5 (PDF)

ps5files.zip (ZIP) (The ZIP file contains: 3 .txt files.)

Problem Set 6 (PDF)

Problem Set 7 (PDF)