Difference between revisions of "VS298: Slides"

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* '''Sep 02 - Introduction'''  
 
* '''Sep 02 - Introduction'''  
**[http://connes.berkeley.edu/~amir/vs298/intro-lecture08.pdf slides]
+
**[http://redwood.berkeley.edu/amir/vs298/intro-lecture08.pdf slides]
  
 
* '''Sep 18 - Supervised learning'''  
 
* '''Sep 18 - Supervised learning'''  
**[http://connes.berkeley.edu/~amir/vs298/superlearn-08.pdf slides]
+
**[http://redwood.berkeley.edu/amir/vs298/superlearn-08.pdf slides]
  
 
* '''Sep 23/25 - Unsupervised learning'''  
 
* '''Sep 23/25 - Unsupervised learning'''  
**[http://connes.berkeley.edu/~amir/vs298/hebb-PCA-08.pdf slides]
+
**[http://redwood.berkeley.edu/amir/vs298/hebb-PCA-08.pdf slides]
  
 
* '''Sep 30/Oct 2 - Sparse coding'''  
 
* '''Sep 30/Oct 2 - Sparse coding'''  
**[http://connes.berkeley.edu/~amir/vs298/sparse-coding-08.pdf slides]
+
**[http://redwood.berkeley.edu/amir/vs298/sparse-coding-08.pdf slides]
  
 
* '''Oct 7 - Sparse coding applications'''  
 
* '''Oct 7 - Sparse coding applications'''  
**[http://connes.berkeley.edu/~amir/vs298/slides_pierre.pdf Pierre's slides]
+
**[http://redwood.berkeley.edu/amir/vs298/slides_pierre.pdf Pierre's slides]
  
 
* '''Oct 14 - Self-organizing maps'''  
 
* '''Oct 14 - Self-organizing maps'''  
**[http://connes.berkeley.edu/~amir/vs298/som-08.pdf slides]
+
**[http://redwood.berkeley.edu/amir/vs298/som-08.pdf slides]
  
 
* '''Oct 16 - Manifold models'''  
 
* '''Oct 16 - Manifold models'''  
**[http://connes.berkeley.edu/~amir/vs298/manifold-08.pdf slides]
+
**[http://redwood.berkeley.edu/amir/vs298/manifold-08.pdf slides]
  
 
* '''Oct 21/23 - Attractor neural networks'''  
 
* '''Oct 21/23 - Attractor neural networks'''  
**[http://connes.berkeley.edu/~amir/vs298/attractor-nets-08.pdf slides]
+
**[http://redwood.berkeley.edu/amir/vs298/attractor-nets-08.pdf slides]
 +
 
 +
* '''Oct 28 - Recurrent neural networks and dynamical systems (David Zipser)'''
 +
**[http://redwood.berkeley.edu/amir/vs298/NNcourseRecNets.pdf slides]
 +
 
 +
* '''Oct 30 - Bayesian probability theory and generative models'''
 +
**[http://redwood.berkeley.edu/amir/vs298/prob-models1.pdf slides]
 +
 
 +
* '''Nov 4 - Mixture of Gaussians model and Boltzmann machines'''
 +
**[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 18 - Supervised learning
  • Sep 23/25 - Unsupervised learning
  • Sep 30/Oct 2 - Sparse coding
  • 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 25 - Spiking neuron models
  • Dec 9 - Encoding meaning with high-dimensional random vectors (Pentti Kanerva)