2.160 | Spring 2006 | Graduate

Identification, Estimation, and Learning


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