Difference between revisions of "VS265: Slides Fall2010"

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* '''Oct 26/28 - Probabilistic/generative models'''  
 
* '''Oct 26/28 - Probabilistic/generative models'''  
**[http://redwood.berkeley.edu/vs265/prob-models.pdf slides]
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**[http://redwood.berkeley.edu/vs265/prob-models-lecture.pdf slides]
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* '''Nov 4/9 - Boltzmann machines'''
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**[http://redwood.berkeley.edu/vs265/boltzmann-machine-slides.pdf slides]
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* '''Nov 16 - ICA and sparse coding'''
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**[http://redwood.berkeley.edu/vs265/sparse-coding2-slides.pdf slides]
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* '''Nov 18 - Kalman filter'''
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**[http://redwood.berkeley.edu/vs265/kalman-slides.pdf slides]
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* '''Nov 23 - Spiking neurons'''
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**[http://redwood.berkeley.edu/vs265/spikes-slides.pdf slides]
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* '''Nov 30 - Computation and coding with neural assemblies  (Kilian Koepsell)'''
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**[http://redwood.berkeley.edu/vs265/kilian-lecture.pdf slides]
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* '''Dec 2 - Hierarchical temporal memory (Jeff Hawkins)'''
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**[http://redwood.berkeley.edu/vs265/Hawkins-lecture.pptx slides]

Latest revision as of 02:44, 28 August 2012

  • Aug 31 - Intro (cont'd) + neuron models
  • Sep 7 - Supervised learning in single-layer and multilayer networks
  • Sep 21 - Supervised learning - continued
  • Sep 21/23 - Unsupervised learning: Hebbian learning and PCA
  • Sep 28/30 - Sparse distributed representation
  • Oct 5 - Self-organizing maps
  • Oct 7 - Manifold models
  • Oct 12/14 - Attractor neural networks
  • Oct 19 - Recurrent networks and dynamical systems - (David Zipser)
  • Oct 21 - Associative memory models (Fritz Sommer)
  • Oct 26/28 - Probabilistic/generative models
  • Nov 4/9 - Boltzmann machines
  • Nov 16 - ICA and sparse coding
  • Nov 23 - Spiking neurons
  • Nov 30 - Computation and coding with neural assemblies (Kilian Koepsell)
  • Dec 2 - Hierarchical temporal memory (Jeff Hawkins)