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)