This graduate course is based on the textbook and monograph Independent Component Analysis (J. Wiley, 2001), written by A. Hyvärinen, J. Karhunen, and E. Oja from our laboratory. The central parts of this book will be covered by lectures given by one of the authors, Prof. Juha Karhunen.
Place: Lecture room T4 in the Dept. of Computer
Science and Engineering of HUT
Time: Mondays 14 - 16, starting on January 16
Credit points: maximum 6
Lecturer: Prof. Juha Karhunen
Assistant: M.Sc. Markus Harva
Web page: http://www.cis.hut.fi/Opinnot/T-61.6030/
Independent Component Analysis (ICA) is one of the most exciting topics in the fields of neural computation, advanced statistics, and statistical signal processing. The textbook as well as the lecture course will provide a comprehensive introduction to this popular technique complete with the mathematical background needed to understand and utilize it. The book and the course offer a general overview of the basics of ICA, important solutions and algorithms, as well as coverage of applications in vision research, brain imaging, and more. The course also deals with related techniques useful in blind signal processing, including methods based on the temporal structure of the data, blind deconvolution, and separation of convolutive mixtures.
You need only the basic knowledge about probability and statistics, matrix algebra, and algorithms in order to follow the course. The first lectures will be used to cover the necessary mathematical background.
To pass the course with 6 credit points (ECTS), you have to
To pass with distinction, you must solve 90 % of your exercise problems in a satisfactory way, and carry out the computer projects providing high quality reports.
Basically the same course has been given for the first time in autumn 2001 (Special Course in Computer and Information Science I, 4 old credit points). Those who have passed that earlier course cannot pass this course.
Please come to the first lecture on Monday, January 16, 2006.
Prof. Juha Karhunen
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