VS265: Slides: Difference between revisions

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==== 4 Sept ====
==== 4 Sept ====
* Chalk board lecture [https://archive.org/details/ucb_vs265_2014_09_04 video]
* Neuron models, membrane equation [https://archive.org/details/ucb_vs265_2014_09_04 video]


==== 11 Sept ====
==== 11 Sept ====
Line 9: Line 9:


==== 16 Sept ====
==== 16 Sept ====
* Neural Networks Intro [http://archive.org/details/SANY0003 video]
* Perceptron model [http://archive.org/details/SANY0003 video]


==== 18 Sept ====
==== 18 Sept ====
Line 16: Line 16:


==== 23,25 Sept ====
==== 23,25 Sept ====
* [http://redwood.berkeley.edu/vs265/hebb-PCA-lecture.pdf Unsupervised learning]
==== Sep 30, Oct 2 ====
* [https://www.dropbox.com/s/t3r5v0ue5e3vv0w/vs265_14_attr2.pdf?dl=0 Attractor Networks]
* [https://archive.org/details/ucb_vs265_2014_09_30_Fritz_Sommer Sept 30 video]
* [https://archive.org/details/VS265-Fall2014-AssociativeMem-2 Oct2nd Video]
==== Oct 7 ====
* [https://archive.org/details/VS265-Fall2014-Oct7-JerryFekdman Jerry Feldman talk]
==== Oct 9 ====
* [https://archive.org/details/ucb_vs265_2014_10_09_Pentti_Kanerva Pentti Kanerva talk]
==== Oct 14 ====
* [https://archive.org/details/ucb_vs265_2014_10_14 WTA, Vector Quantization]
==== Oct 16 ====
* [http://cs.brown.edu/people/tld/note/blog/14/10/16/index.html Lecture notes and slides]
* [https://archive.org/details/VS265-Fall2014-Oct16-TomDean Talk video]
==== Oct 21,23,28 ====
* Sparse coding [http://redwood.berkeley.edu/vs265/sparse-coding-slides.pdf slides]
* [https://archive.org/details/VS265-Fall2014-Oct23 Oct23rd video]
* [https://archive.org/details/VS265-Fall2014-Oct28 Oct28th video]
==== Oct 30, Nov. 4, 6 ====
* [https://archive.org/details/VS265-Oct30 Oct30 Video]
* [https://archive.org/details/VS265-Fall14-Nov4 Nov4 Video]
* [https://archive.org/details/VS265-Fall14-Nov6 Nov6 Video]
* [https://archive.org/details/VS265-Nov13-Fall2014 Nov 13 Video]
* [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)]
==== Nov 13 ====
* Attractor neural nets [http://redwood.berkeley.edu/vs265/attractor-lecture.pdf slides]
==== Nov 18 ====
* Guy Isely [http://redwood.berkeley.edu/vs265/Guy-Isely-neurocomputation-rnns.pdf slides]
* Brian Cheung [http://redwood.berkeley.edu/vs265/Brian-Cheung-LSTMS.pdf slides]
* [https://archive.org/details/VS265-Fall14-Nov18 video]
==== Nov 20 ====
* Probabilistic Models [http://redwood.berkeley.edu/vs265/prob-models-lecture.pdf slides]
* [https://archive.org/details/VS265-Fall14-Nov20 video]
==== Nov 25 ====
* Boltzmann machine [http://redwood.berkeley.edu/vs265/boltzmann-machine.pdf slides]
* [https://archive.org/details/VS265-Fall14-Nov25 Nov 25]
* [https://archive.org/details/VS265-Fall14-Dec2 Dec 2]
==== Dec 4 ====
* [https://archive.org/details/VS265-Fall14-Dec4 ICA Talk by Tony Bell]
==== Dec 9 ====
* Kalman filter [http://redwood.berkeley.edu/vs265/kalman-slides.pdf slides]
* Spiking neurons [http://redwood.berkeley.edu/vs265/spikes-slides.pdf slides]
* [https://archive.org/details/VS265-Fall14-Dec9 lecture video]
* [http://redwood.berkeley.edu/w/images/9/92/Adelson.pdf tmp]
* [[File:x.pdf]]

Latest revision as of 20:59, 26 June 2015

28 Aug

4 Sept

  • Neuron models, membrane equation video

11 Sept

  • Paul Rhodes guest lecture video

16 Sept

18 Sept

23,25 Sept

Sep 30, Oct 2

Oct 7

Oct 9

Oct 14

Oct 16

Oct 21,23,28

Oct 30, Nov. 4, 6

Nov 13

Nov 18

Nov 20

Nov 25

Dec 4

Dec 9

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