Blind Source Separation

Welcome, dear students!

Here you will find resources for the course, including slides and assignments.

Please check this page regularly for updates throughout the semester.

Reference:

• INDEPENDENT COMPONENT ANALYSIS,
  Aapo Hyvärinen, Juha Karhunen, Erkki Oja,
  John Wiley & Sons, Inc., 2001.

Qualification:

• Assignments

• Final Exam

• Presentation

• Project

Contents:

• Chapter 1. Introduction

• Chapter 2. Random Vectors and Independence

• Chapter 3. Gradients and Optimization Methods

• Chapter 4. Estimation Theory

• Chapter 5. Information Theory

• Chapter 6. Principal Component Analysis and Whitening

• Chapter 7. What is Independent Component Analysis?

• Chapter 8. ICA by Maximization of Nongaussianity

• Chapter 9. ICA by Maximum Likelihood Estimation

• Chapter 10. ICA by Minimization of Mutual Information

• Chapter 11. ICA by Tensorial Methods

• Chapter 12. ICA by Nonlinear Decorrelation and Nonlinear PCA

• Chapter 13. Noisy ICA

• Chapter 14. ICA with Overcomplete Bases

• Chapter 15. Nonlinear ICA

• Chapter 16. ICA Methods Using Time Structure

• Chapter 17. Convolutive Mixtures and Blind Deconvolution

• Chapter 18. Feature Extraction by ICA

• Chapter 19. Brain Imaging Applications of ICA

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