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