2012 mdsp pr01　introduction 0921

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Multi-Dimensional Signal Processing ＆

Pattern Recognition

2012 Fall Semester International Course Lecture

Nozomu HAMADA 浜田 望

Course Introduction

• Course Plan (Calendar)

• Lecture Note, Textbook, Grades, etc.

• Course Overview

Part 1 : Bayesian Signal Processing

Part 2 : Pattern Recognition

Machine learning

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Course Calendar Class DATE Contents

1 Sep. 26 Course information & Course overview

2 Oct. 4 Bayes Estimation

3 〃 11 Classical Bayes Estimation - Kalman Filter -

4 〃 18 Simulation-based Bayesian Methods

5 〃 25 Modern Bayesian Estimation Particle Filter

6 Nov. 1 HMM(Hidden Markov Model)

Nov. 8 No Class

7 〃 15 Supervised Learning

8 〃 29 Bayesian Decision

9 Dec. 6 PCA(Principal Component Analysis)

10 〃 13 ICA(Independent Component Analysis)

11 〃 20 Applications of PCA and ICA

12 〃 27 Clustering, k-means et al.

13 Jan. 17 Other Topics 1 Kernel machine.

14 〃 22(Tue) Other Topics 2

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Prerequisites

Elementary of Discrete-time signals and Systems

Elementary of Probability/statistics and Matrix Theory

References:

1) J. V. Candy “Bayesian Signal Processing” Wiley 2009

2) B.Ristic, et. al. “Beyond the Kalman Filter”, Artech house 2004

3) R.O. Duda, P.E. Hart, and D. G. Stork, “Pattern Classification”,

John Wiley & Sons, 2nd edition, 2004

4) C. M. Bishop, “Pattern Recognition and Machine Learning”,

Springer, 2006

5) E. Alpaydin, Introduction to Machine Learning, MIT Press, 2009

6) A. Huvarinen et. al., ”Independent Component Analysis”

Wiley-Interscience 2001

Japanese textbooks

Japanese translations of 3)，4)，6)

片山徹、「非線形カルマンフィルタ」 朝倉書店 2011

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E. Alpaydin,

Introduction to

Machine Learning,

MIT Press, 2009

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■Grading:

Homeworks

Report at the end of the term (Jan. 2013)

- Discuss some subjects -

■Lecture Note

All lecture slides and handouts are available at the

keio jp. web site “class support”.

■E-mail

hamada@sd.keio.ac.jp

■Office

Room 25-418

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Part 1 : Bayesian Signal Processing

- Bayesian Estimation, Kalman Filter, Monte Carlo，Particle Filter -

Part 2: Pattern Recognition

- Bayesian Decision，PCA & ICA, Clustering, Karnel Methods -

Object Tracking Problem

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Handwritten

Digit

Recognition

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Face recognition problem

Given a training database

of facial photographs with

identification tags on that.

Design an automated system

to recognize the identity of a

new image

of the person