| Date | Speakers | Topics |
| 28.9. |
Patrik Hoyer Mika Inki |
Session #1: Redundancy reduction and ICA "Unsupervised Learning" & "Finding minimum entropy codes" (H Book Chapters 1 & 14) ICA Tutorial (extra material) |
| 5.10. |
Vesa Siivola Jarkko Salojärvi |
Session #2: Information maximization "Local synaptic learning rules suffice to maximize mutual information in a linear network" (H Book Chapter 2) "An information-maximization approach to blind separation and blind deconvolution" (H Book Chapter 8) |
| 12.10. |
Jarkko Venna Markus Koskela Teemu Hirsimäki |
Session #3: Image feature extraction by sparse coding and ICA "What is the goal of sensory coding?" (H Book Chapter 7) "The `independent components' of natural scenes are edge filters" (additional material) "Sparse coding with an overcomplete basis set: A strategy employed by V1?" (additional material) |
| 19.10. |
Jaakko Särelä Jarmo Hiipakka |
Session #4: Analyzing brain signals by ICA "Searching for independence in electromagnetic brain waves" (G Book Chapter 10) "Analysis of fMRI by blind separation into independent spatial components" (additional material) |
| 26.10. |
Harri Kalaja Yuan Zhijian Jaakko Peltonen |
Session #5: Text document analysis by ICA "ICA on noisy data: A factor analysis approach" (G Book Chapter 11) [CANCELLED!] "Independent components in text" (G Book Chapter 13) ICA of text documents (TechRep by Mark Girolami) |
| 2.11. |
Matti Aksela Heikki Astola |
Session #6: Learning invariant image features "Learning invariance from transformation sequences" (H Book Chapter 5) The adaptive-subspace self-organizing map (additional material) |
| 9.11. |
Juha Karvanen Janne Nikkilä |
Session #7: Analyzing dependencies in ICA image features Independent subspace analysis (additional material) "The independence assumption: Analyzing the independence of the components by topography" (G Book Chapter 3) |
| 16.11. |
Tapani Raiko Antti Honkela |
Session #8: Bayesian methods applied to ICA Tutorial on Bayesian methods (additional material) & "Ensemble learning" (G Book Chapter 5) "Bayesian non-linear independent component analysis by multi-layer perceptrons" (G Book Chapter 6) |
| 23.11. |
Ville Viitaniemi Paulo Esquef |
Session #9: Coherence as a basis for learning "Learning perceptually salient visual parameters using spatiotemporal smoothness constraints" (H Book chapter 6) "Learning mixture models of spatial coherence" (H Book chapter 12) |
| 30.11. |
Vuokko Vuori Ramunas Girdziusas |
Session #10: More on learning features "Emergence of position-independent detectors of sense of rotation and dilation with hebbian learning: An analysis" (H Book Chapter 4) "Independent component analysis of textures" (additional material) |