Lec # | Topics | Key Dates |
---|---|---|
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 |
Calendar
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Spring
2006
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