1 |
Introduction |
|
Part I: Estimation |
2 |
Recursive Least Square (RLS) Algorithms |
|
3 |
Properties of RLS |
|
4 |
Random Processes, Active Noise Cancellation |
|
5 |
Discrete Kalman Filter-1 |
Problem set 1 due |
6 |
Discrete Kalman Filter-2 |
|
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 Time-invariant Systems |
|
11 |
Time Series Data Compression, Laguerre Series Expansion |
Problem set 4 due |
12 |
Non-linear Models, Function Approximation Theory, Radial Basis Functions |
|
13 |
Neural Networks |
Problem set 5 due |
|
Mid-term 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, Cramer-Rao Lower Bound and Best Unbiased Estimate |
Problem set 7 due |
21 |
Information Theory of System Identification: Kullback-Leibler Information Distance, Akaike's Information Criterion |
|
|
Final Exam |
|