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Laboratory of Computer and Information Science / Neural Networks Research Centre CIS Lab Helsinki University of Technology

Courses in previous years: [ 1998 | 2000 | 2002 | 2003 | 2004 | 2005 | 2006 ]


T-61.6030 Special Course in Computer and Information Science III L:

Introductory Elements of Functional Data Analysis


Lecturers PhD (Eng.) Francesco Corona
PhD (Eng.) Amaury Lendasse
Assistant M.Sc. Elia Liitiäinen
Credits (ECTS) 7
Semester Spring 2007 (during periods III and IV)
Seminar sessions On tuesdays 14-16 in computer science building,
Konemiehentie 2, Otaniemi, Espoo. The first session is on 23.1.2007 in lecture hall T4.
Language English
Web http://www.cis.hut.fi/Opinnot/T-61.6030/
E-mail eliitiai (at) cc.hut.fi
lendasse (at) hut.fi

Introduction

Functional data analysis (FDA) is a framework for analyzing data in function spaces. Instead of discrete vectors, the data consists of functions and is thus infinite dimensional. FDA offers statistical tools for handling the high dimensionality of the data using smoothness properties of the functions. Functional data analysis combines statistical methods with well-known mathematical techniques for analyzing curves. The usefulness of these methods depends largely on the smoothness of the functions.

Many classical statistical and machine learning methods have their functional counterparts. Often this type of generalization of well-known methods leads to surprisingly elegant interpretations, which show totally new ways to use the methods.

Functional data analysis is not only of theoretical interest, but arises from practical needs. Functional data is common for example in chemometry, econometrics and machine learning. Further information on FDA can be found here.

Requirements for passing the course

Each student gives a presentation in the seminar. In addition, requirements include a project work and active participation in the lectures (one absence is allowed).

Course Material

We will use the following two books:

[1] J. O. Ramsay and B. W. Silverman. Functional Data Analysis, second edition. Springer, New York. 2005.
[2] F. Ferraty and P. Vieu. Non-parametric Functional Data Analysis. Springer. 2006.

The books can be ordered for example at http://booky.fi.

Schedule


Time Lecturer Topic Slides
23.1. Amaury Lendasse Presentation of the Course slides
30.1. Elia Liitiäinen Ramsay: Chapters 1,2,3 slides
6.2. Yoan Miche Ramsay: Chapters 4,5,6 slides
13.2 Ville Turunen Ramsay: Chapters 7,8,9 slides
20.2 Markus Kuusisto Ramsay: Chapter 10,11,12 slides
27.2 Antti Sorjamaa Ramsay: Chapters 13,14,15 slides
6.3 Exam week
13.3 Zhirong Yang Ramsay: Chapters 16,17,18 slides
20.3 Francesco Corona Presentation of the project slides
27.3 Elia Liitiäinen and Yoan Miche Ferraty: Chapters 1,2,3,4 slides1, slides2
3.4 Markus Kuusisto and Ville Turunen Ferraty: Chapters 5,6,7 slides1, slides2
10.4 Easter vacation
17.4 Antti Sorjamaa and Zhirong Yang Ferraty: Chapters 8,9,12 slides1, slides2, slides3


Project work

Information about the project work is available here.

For more information concerning the course, please send email to eliitiai (at) cc.hut.fi or lendasse (at) hut.fi.

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