VS298: Slides: Difference between revisions
From RedwoodCenter
Jump to navigationJump to search
No edit summary |
No edit summary |
||
(12 intermediate revisions by the same user not shown) | |||
Line 1: | Line 1: | ||
Many lectures from Oct 7th on are available in video form thanks to Jeff Teeters. Please check [http://www.archive.org/search.php?query=ucbvs298_neural_comp here]. | |||
* '''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''' | |||
**[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 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)