# Difference between revisions of "VS265: Slides Fall2010"

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* '''Sep 7 - Supervised learning in single-layer and multilayer networks''' | * '''Sep 7 - Supervised learning in single-layer and multilayer networks''' | ||

**[http://redwood.berkeley.edu/vs265/superlearn.pdf slides] | **[http://redwood.berkeley.edu/vs265/superlearn.pdf slides] | ||

+ | |||

+ | * '''Sep 21 - Supervised learning - continued''' | ||

+ | **[http://redwood.berkeley.edu/vs265/superlearn2.pdf slides] | ||

+ | |||

+ | * '''Sep 21/23 - Unsupervised learning: Hebbian learning and PCA''' | ||

+ | **[http://redwood.berkeley.edu/vs265/hebb-PCA-lecture.pdf slides] | ||

+ | |||

+ | * '''Sep 28/30 - Sparse distributed representation''' | ||

+ | **[http://redwood.berkeley.edu/vs265/sparse-coding-slides.pdf slides] | ||

+ | |||

+ | * '''Oct 5 - Self-organizing maps''' | ||

+ | **[http://redwood.berkeley.edu/vs265/som-lecture.pdf slides] | ||

+ | |||

+ | * '''Oct 7 - Manifold models''' | ||

+ | **[http://redwood.berkeley.edu/vs265/manifold-models-lecture.pdf slides] | ||

+ | **[http://redwood.berkeley.edu/vs265/adaptation-lecture.pdf slides] (an aside on adaptation) | ||

+ | |||

+ | * '''Oct 12/14 - Attractor neural networks''' | ||

+ | **[http://redwood.berkeley.edu/vs265/attractor-lecture.pdf slides] | ||

+ | |||

+ | * '''Oct 19 - Recurrent networks and dynamical systems - (David Zipser)''' | ||

+ | **[http://redwood.berkeley.edu/vs265/zipser-lecture.ppt slides] | ||

+ | |||

+ | * '''Oct 21 - Associative memory models (Fritz Sommer)''' | ||

+ | **[http://redwood.berkeley.edu/vs265/sommer-lecture.pdf slides] | ||

+ | |||

+ | * '''Oct 26/28 - Probabilistic/generative models''' | ||

+ | **[http://redwood.berkeley.edu/vs265/prob-models-lecture.pdf slides] | ||

+ | |||

+ | * '''Nov 4/9 - Boltzmann machines''' | ||

+ | **[http://redwood.berkeley.edu/vs265/boltzmann-machine-slides.pdf slides] | ||

+ | |||

+ | * '''Nov 16 - ICA and sparse coding''' | ||

+ | **[http://redwood.berkeley.edu/vs265/sparse-coding2-slides.pdf slides] | ||

+ | |||

+ | * '''Nov 18 - Kalman filter''' | ||

+ | **[http://redwood.berkeley.edu/vs265/kalman-slides.pdf slides] | ||

+ | |||

+ | * '''Nov 23 - Spiking neurons''' | ||

+ | **[http://redwood.berkeley.edu/vs265/spikes-slides.pdf slides] | ||

+ | |||

+ | * '''Nov 30 - Computation and coding with neural assemblies (Kilian Koepsell)''' | ||

+ | **[http://redwood.berkeley.edu/vs265/kilian-lecture.pdf slides] | ||

+ | |||

+ | * '''Dec 2 - Hierarchical temporal memory (Jeff Hawkins)''' | ||

+ | **[http://redwood.berkeley.edu/vs265/Hawkins-lecture.pptx slides] |

## Latest revision as of 02:44, 28 August 2012

**Aug 26 - Introduction**

**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 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 18 - Kalman filter**

**Nov 23 - Spiking neurons**

**Nov 30 - Computation and coding with neural assemblies (Kilian Koepsell)**

**Dec 2 - Hierarchical temporal memory (Jeff Hawkins)**