| 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 | |