Identification, Estimation, and Learning

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