# Difference between revisions of "VS298: Slides"

From RedwoodCenter

Jump to navigationJump to search(8 intermediate revisions by the same user not shown) | |||

Line 2: | Line 2: | ||

* '''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] | ||

+ | |||

+ | * '''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)**