| 1 |
Introduction |
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| Part I: Estimation |
| 2 |
Recursive Least Square (RLS) Algorithms |
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| 3 |
Properties of RLS |
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| 4 |
Random Processes, Active Noise Cancellation |
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| 5 |
Discrete Kalman Filter-1 |
Problem set 1 due |
| 6 |
Discrete Kalman Filter-2 |
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| 7 |
Continuous Kalman Filter |
Problem set 2 due |
| 8 |
Extended Kalman Filter |
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| Part 2: Representation and Learning |
| 9 |
Prediction Modeling of Linear Systems |
Problem set 3 due |
| 10 |
Model Structure of Linear Time-invariant Systems |
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| 11 |
Time Series Data Compression, Laguerre Series Expansion |
Problem set 4 due |
| 12 |
Non-linear Models, Function Approximation Theory, Radial Basis Functions |
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| 13 |
Neural Networks |
Problem set 5 due |
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Mid-term Exam |
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| 14 |
Error Back Propagation Algorithm |
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| Part 3: System Identification |
| 15 |
Perspective of System Identification, Frequency Domain Analysis |
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| 16 |
Informative Data Sets and Consistency |
Problem set 6 due |
| 17 |
Informative Experiments: Persistent Excitation |
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| 18 |
Asymptotic Distribution of Parameter Estimates |
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| 19 |
Experiment Design, Pseudo Random Binary Signals (PRBS) |
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| 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 |
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Final Exam |
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