# Calendar

Course calendar.
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 |