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T-61.181 Special course in Information Science I

INDEPENDENT COMPONENT ANALYSIS, AUTUMN 2001

TENTATIVE LECTURE SCHEDULE AND COURSE PLAN

Date Topic Lecturer
Lecture 1
17.9.
General information. Chapter 1: Introduction Erkki Oja
Chapter 2: Random vectors and independence, part 1 Juha Karhunen
Lecture 2
24.9.
Chapter 2: Random vectors and independence, part 2 Juha Karhunen
Chapter 3: Gradients and optimization methods Erkki Oja
Lecture 3
01.10.
Chapter 4: Estimation theory Juha Karhunen
Chapter 5: Information theory Juha Karhunen
Lecture 4
08.10.
Chapter 6: Principal component anal. and whitening Erkki Oja
Chapter 7: What is independent component analysis? Erkki Oja
Lecture 5
15.10.
Chapter 8: ICA by maximization of nongaussianity Juha Karhunen
Chapter 8: ICA by maximization of nongauss., part 2 Juha Karhunen
Lecture 6
25.10.
Chapter 9: ICA by maximum likelihood estimation Erkki Oja
Chapter 10: ICA by minimization of mutual inform. Erkki Oja
Lecture 7
29.10.
Chapter 11: ICA by tensorial methods Erkki Oja
Chapter 12: ICA by nonlinear decorrelation and ... Erkki Oja
Lecture 8
5.11.
Chapter 13: Practical considerations Erkki Oja
Chapter 14: Overview and comparison of basic ... Erkki Oja
Lecture 9
12.11.
Chapter 15: Noisy ICA Juha Karhunen
Chapter 16: ICA with overcomplete bases Juha Karhunen
Lecture 10
19.11.
Chapter 17: Nonlinear ICA Juha Karhunen
Chapter 18: Methods using time structure, part 1 Juha Karhunen
Lecture 11
26.11.
Chapter 18: Methods using time structure, part 2 Juha Karhunen
Chapter 19: Convolutive mixtures and blind ... Juha Karhunen
Lecture 12
3.12.
Chapter 21: Feature extraction by ICA Erkki Oja
Chapter 22: Brain imaging applications Erkki Oja

Chapters 20, 23, and 24 of the textbook A. Hyvärinen, J. Karhunen, and E. Oja, "Independent Component Analysis", J. Wiley 2001, 481+xxii pages, will be skipped. Lectures will be held in the room Y313 in the main building of HUT.



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Tuesday, 16-Oct-2001 14:27:51 EEST