VS298: Slides: Difference between revisions
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* '''Sep 02 - Introduction''' | * '''Sep 02 - Introduction''' | ||
**[http:// | **[http://redwood.berkeley.edu/amir/vs298/intro-lecture08.pdf slides] | ||
* '''Sep 18 - Supervised learning''' | * '''Sep 18 - Supervised learning''' | ||
**[http:// | **[http://redwood.berkeley.edu/amir/vs298/superlearn-08.pdf slides] | ||
* '''Sep 23/25 - Unsupervised learning''' | * '''Sep 23/25 - Unsupervised learning''' | ||
**[http:// | **[http://redwood.berkeley.edu/amir/vs298/hebb-PCA-08.pdf slides] | ||
* '''Sep 30/Oct 2 - Sparse coding''' | * '''Sep 30/Oct 2 - Sparse coding''' | ||
**[http:// | **[http://redwood.berkeley.edu/amir/vs298/sparse-coding-08.pdf slides] | ||
* '''Oct 7 - Sparse coding applications''' | * '''Oct 7 - Sparse coding applications''' | ||
**[http:// | **[http://redwood.berkeley.edu/amir/vs298/slides_pierre.pdf Pierre's slides] | ||
* '''Oct 14 - Self-organizing maps''' | * '''Oct 14 - Self-organizing maps''' | ||
**[http:// | **[http://redwood.berkeley.edu/amir/vs298/som-08.pdf slides] | ||
* '''Oct 16 - Manifold models''' | * '''Oct 16 - Manifold models''' | ||
**[http:// | **[http://redwood.berkeley.edu/amir/vs298/manifold-08.pdf slides] | ||
* '''Oct 21/23 - Attractor neural networks''' | * '''Oct 21/23 - Attractor neural networks''' | ||
**[http:// | **[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] | |||
Revision as of 06:45, 6 November 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
