VS265: Slides Fall2012: Difference between revisions
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==== 27 Aug ==== | ==== 27 Aug ==== | ||
* [http://redwood.berkeley.edu/vs265/ | * [http://redwood.berkeley.edu/vs265/intro-lecture.pdf Introduction] | ||
==== 29 Aug ==== | |||
* [http://redwood.berkeley.edu/vs265/intro-lecture2.pdf Introduction continued] | |||
==== 5 Sept ==== | |||
* [http://redwood.berkeley.edu/vs265/synapses.pdf Synapses, brains and machines] | |||
==== 17/19 Sept ==== | |||
* [http://redwood.berkeley.edu/vs265/superlearn.pdf Supervised learning] | |||
==== 24 Sept - 1 Oct ==== | |||
* [http://redwood.berkeley.edu/vs265/hebb-PCA-lecture.pdf Unsupervised learning] | |||
==== 8 Oct - 15 Oct ==== | |||
* [http://redwood.berkeley.edu/vs265/sparse-coding-slides.pdf Sparse distributed representation] | |||
==== 17 Oct - 22 Oct ==== | |||
* [http://redwood.berkeley.edu/vs265/som-lecture.pdf Self-organizing maps] | |||
* [http://redwood.berkeley.edu/vs265/manifold-models-lecture.pdf Manifold models] | |||
* [http://redwood.berkeley.edu/vs265/adaptation-lecture.pdf (an aside on adaptation)] | |||
==== 24 Oct ==== | |||
* [http://redwood.berkeley.edu/vs265/attractor-lecture.pdf Attractor neural networks] | |||
==== 29 Oct ==== | |||
* [http://redwood.berkeley.edu/vs265/hillar-hopfieldmpf.pdf Little-Hopfield Memory Storage with Minimum Probability Flow] (Chris Hillar guest lecture) | |||
==== 5 Nov ==== | |||
* [http://redwood.berkeley.edu/vs265/prob-models-lecture.pdf Probabilistic/generative models] | |||
==== 19 Nov ==== | |||
* [http://redwood.berkeley.edu/vs265/boltzmann-machine.pdf Boltzmann machine] | |||
==== 21 Nov ==== | |||
* [http://redwood.berkeley.edu/vs265/sparse-coding2-slides.pdf Sparse Coding and 'ICA'] | |||
Latest revision as of 18:48, 28 August 2014
27 Aug
29 Aug
5 Sept
17/19 Sept
24 Sept - 1 Oct
8 Oct - 15 Oct
17 Oct - 22 Oct
24 Oct
29 Oct
- Little-Hopfield Memory Storage with Minimum Probability Flow (Chris Hillar guest lecture)
