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 featuresIndependent 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 ICATutorial 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) |

NOTE: "H Book" refers to Hinton & Sejnowski (eds): "Unsupervised Learning",

while "G Book" refers to Girolami (ed): "Advances in Independent Component Analysis"