# VS298: Slides: Difference between revisions

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* '''Nov 4 - Mixture of Gaussians model and Boltzmann machines''' | * '''Nov 4 - Mixture of Gaussians model and Boltzmann machines''' | ||

**[http://redwood.berkeley.edu/amir/vs298/prob-models2.pdf slides] | **[http://redwood.berkeley.edu/amir/vs298/prob-models2.pdf slides] | ||

* '''Nov 18 - Sparse coding and ICA''' | |||

**[http://redwood.berkeley.edu/amir/vs298/sparse-coding-ica.pdf slides] | |||

* '''Nov 20 - Kalman filter''' | |||

**[http://redwood.berkeley.edu/amir/vs298/kalman.pdf slides] | |||

* '''Nov 25 - Spiking neuron models''' | |||

**[http://redwood.berkeley.edu/amir/vs298/spikes.pdf slides] | |||

* '''Dec 9 - Encoding meaning with high-dimensional random vectors (Pentti Kanerva)''' | |||

**[http://redwood.berkeley.edu/amir/vs298/pentti-slides.pdf slides] |

## Latest revision as of 05:29, 11 December 2008

Many lectures from Oct 7th on are available in video form thanks to Jeff Teeters. Please check here.

**Sep 02 - Introduction**

**Sep 18 - Supervised learning**

**Sep 23/25 - Unsupervised learning**

**Sep 30/Oct 2 - Sparse coding**

**Oct 7 - Sparse coding applications**

**Oct 14 - Self-organizing maps**

**Oct 16 - Manifold models**

**Oct 21/23 - Attractor neural networks**

**Oct 28 - Recurrent neural networks and dynamical systems (David Zipser)**

**Oct 30 - Bayesian probability theory and generative models**

**Nov 4 - Mixture of Gaussians model and Boltzmann machines**

**Nov 18 - Sparse coding and ICA**

**Nov 20 - Kalman filter**

**Nov 25 - Spiking neuron models**

**Dec 9 - Encoding meaning with high-dimensional random vectors (Pentti Kanerva)**