- First class on Wed Aug 16, 9:30 AM at EE404
- please read the course syllabus

the handouts are still under revision.

- Introduction
- Review on linear system and random processes
- Review on linear algebra
- Model parametrization
- Input signals
- Linear least-squares
- Significance tests for LS
- Variations on least-squares
- Instrument variable methods
- Prediction error methods
- Statistical estimation
- Subspace methods
- Model selection and model validation
- Recursive identification methods

the topics not taught currently

Each year we aim to assign the problems of fitting models to real-world data as group assignments. Students learn a general description of the problem before the midterm and the project starts after the midterm. Students develop the problem statement, estimation formulation, experimental results and write the report throughout the six weeks until the final week. Each group writes a progress report and present it weekly. Students should apply the estimation techniques learned from class or more advanced/alternative methods from the research literature. The details of project guidelines are described in the project instruction. The full reports from the previous years include:

- Estimation of Autoregressive with Exogeneous Inputs Model for fMRI Data

- Contributors: Nattaporn Plub-in/ Patawee Prakrankamanant/ Nop Polboon/ Ranyaphat Hongpipatsak/ Morokot Cheat

- Solar Forecasting using Time Series Models

- Contributors: Maxime Facquet/ Supachai Suksamosorn/ Veenakorn Suphatsatienkul/ Vichaya Layanun

- A Convex Formulation of Structural Equation Modeling (SEM) in fMRI Study

- Contributors: Anupon Pruttiakaravanich/ Auangkun Rangsikunpum/ Pusit Suriyavejwongs/ Tawan Luprasong

- Parameter estimation of Gumbel distribution for flood peak data

- Contributors: Piyatida Hoisungwan (co-advisor from Dept. of Water Resource)/ Jitin Khemwong/ Tiwat Boonyawiwat/ Tanakorn Kriengkomol

- Rainfall Grid Interpolation from Rain Gauge and Rainfall Predicted from Satellite Data

- Contributors: Piyatida Hoisungwan (co-advisor from Dept. of Water Resource)/ Petchakrit Pinyopawasutthi/ Pongsorn Keadtipod/ Tanut Aranchayanont

- Fitting Vector Auto Regressive Model to Electroencephalography (EEG) Signals

- Contributors: Nuntanut Raksasri/ Akasit Aupaiboon/ Pawarisson Jaitahan

- Least-squares fitting: data-LScostheta.mat
- State trajectory estimation: data-state-traj-est.mat
- Estimation of initial conditions: data-state-traj-est.mat
- Least-squarse fitting: data-ls-fitting.mat
- Estimation of a first-order system: data-est1storder.mat
- Estimation of scalar AR proceses: nikkei_feb11_feb12.mat
- Estimation of stable vector AR processes: data-vec-ar.mat and data-vec-ar-short.mat
- MAP estimation of a linear model: data-map-linmodel
- Recursive least-squares with a forgetting factor: data-rls-ff
- Comparison between recursive least-squares and recursive instrument variable method: data-rls-riv
- MAP estimation of an ARX model: data-arxmap
- Simulation and One-Step Prediction: data-predicted-simulated.mat
- Topology selection in Gaussian graphical model: data-gaussian-gm
- Choosing scalar AR model order: data-modelsel-ar
- Choosing the effective length of FIR models: data-modelsel-FIR
- Consistency of IV and LS estimates: data-consistent-iv3
- Optimal Nonlinear estimator: data-nonlinest1
- Optimal Nonlinear estimator: data-nonlinest
- Robust least-squares with Laplacian uncertainty: data-nonlinest1
- Compare the bias and variance of sparse LS estimate: data-cmp-var-bias-sparseLS
- Estimation of time series trends: data-ls-season.mat

Lectures: | EE404, Mon Wed 9:30 - 11 am |
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Course Syllabus: | |

Textbooks: |

- Soderstrom and P. Stoica,
*System Identification, Prentice Hall*. 1989 (available for download from the author website)

- Soderstrom and P. Stoica,
- Ljung,
*System Identification: Theory for the User*, 2nd edition, Prentice Hall, 1999

- Ljung,
- J.P. Norton,
*An Introduction to Identification*, Dover, 1986 - P.Van Overschee and B.D. Moor,
*Subspace identification for linear systems: Theory—Implementation—Applications*, Kluwer Academic Publishers, 2012 - James and D. Witten, T. Hastie, and R. Tibshirani,
*An Introduction to Statistical Learning with Applications in R*, Springer, 2013

- James and D. Witten, T. Hastie, and R. Tibshirani,

Related textbooks: | |
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- R.A. Horn and C.R. Johnson,
*Matrix Analysis*, 2nd edition, Cambridge, 2013 - Strang,
*Linear Algebra and Its Applications*, 3rd edition, Harcourt Brace Jovanovich, 1988

- Strang,
- R.E. Schumacker and R.G. Lomax,
*A Beginner Guide to Structural Equation Modeling*, 3rd Edition, Routledge, 2010 - R.B. Kline,
*Principles and Practice of Structural Equation Modeling*, 3rd Edition, Guilford, 2011 - Raykov and G.A. Marcoulides,
*A First Course in Structural Equation Modeling*, 2nd Edition, Lawrence Erlbaum Associates, 2006

- Raykov and G.A. Marcoulides,

Grading: | Homework 30% Midterm 30% Final 10% Project 30% |
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Material: | MATLAB Tutorial |