1 
Introduction 

Part I: Estimation 
2 
Recursive Least Square (RLS) Algorithms 

3 
Properties of RLS 

4 
Random Processes, Active Noise Cancellation 

5 
Discrete Kalman Filter1 
Problem set 1 due 
6 
Discrete Kalman Filter2 

7 
Continuous Kalman Filter 
Problem set 2 due 
8 
Extended Kalman Filter 

Part 2: Representation and Learning 
9 
Prediction Modeling of Linear Systems 
Problem set 3 due 
10 
Model Structure of Linear Timeinvariant Systems 

11 
Time Series Data Compression, Laguerre Series Expansion 
Problem set 4 due 
12 
Nonlinear Models, Function Approximation Theory, Radial Basis Functions 

13 
Neural Networks 
Problem set 5 due 

Midterm Exam 

14 
Error Back Propagation Algorithm 

Part 3: System Identification 
15 
Perspective of System Identification, Frequency Domain Analysis 

16 
Informative Data Sets and Consistency 
Problem set 6 due 
17 
Informative Experiments: Persistent Excitation 

18 
Asymptotic Distribution of Parameter Estimates 

19 
Experiment Design, Pseudo Random Binary Signals (PRBS) 

20 
Maximum Likelihood Estimate, CramerRao Lower Bound and Best Unbiased Estimate 
Problem set 7 due 
21 
Information Theory of System Identification: KullbackLeibler Information Distance, Akaike's Information Criterion 


Final Exam 
