VS265: Reading Fall2010: Difference between revisions
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==== 26 Aug ==== | ==== 26 Aug ==== | ||
* Dreyfus, H.L. and Dreyfus, S.E. [http://redwood.berkeley.edu/ | * Dreyfus, H.L. and Dreyfus, S.E. [http://redwood.berkeley.edu/vs265/DreyfusDreyfus.pdf ''Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint'']. Daedalus, Winter 1988. | ||
* Bell, A.J. ''Levels and loops: the future of artificial intelligence and neuroscience''. Phil Trans: Bio Sci. '''354''':2013--2020 (1999) [http://dx.doi.org/10.1098/rstb.1999.0540 here] or [http://www.cnl.salk.edu/~tony/ptrsl.pdf here] | * Bell, A.J. ''Levels and loops: the future of artificial intelligence and neuroscience''. Phil Trans: Bio Sci. '''354''':2013--2020 (1999) [http://dx.doi.org/10.1098/rstb.1999.0540 here] or [http://www.cnl.salk.edu/~tony/ptrsl.pdf here] | ||
Optional: | Optional: | ||
* Land, MF and Fernald, RD. [http://redwood.berkeley.edu/ | * Land, MF and Fernald, RD. [http://redwood.berkeley.edu/vs265/landfernald92.pdf The Evolution of Eyes], Ann Revs Neuro, 1992. | ||
==== 31 Aug ==== | ==== 31 Aug ==== | ||
* Mead, C. [http://redwood.berkeley.edu/ | * Mead, C. [http://redwood.berkeley.edu/vs265/Mead.pdf Chapter 1: Introduction] and [http://redwood.berkeley.edu/vs265/Neurons.pdf Chapter 4: Neurons] from ''Analog VLSI and Neural Systems'', Addison-Wesley, 1989. | ||
* [http://redwood.berkeley.edu/ | * [http://redwood.berkeley.edu/vs265/lti-conv/lti-convolution.html Linear time-invariant systems and convolution] | ||
* [http://redwood.berkeley.edu/ | * [http://redwood.berkeley.edu/vs265/diffeq-sim/diffeq-sim.html Simulating differential equations] | ||
* [http://redwood.berkeley.edu/ | * [http://redwood.berkeley.edu/vs265/dynamics/dynamics.html Dynamics] | ||
* Carandini M, Heeger D (1994) [http://redwood.berkeley.edu/ | * Carandini M, Heeger D (1994) [http://redwood.berkeley.edu/vs265/carandini-heeger.pdf Summation and division by neurons in primate visual cortex.] Science, 264: 1333-1336. | ||
==== 02 Sep ==== | ==== 02 Sep ==== | ||
* Jordan, M.I. [http://redwood.berkeley.edu/ | * Jordan, M.I. [http://redwood.berkeley.edu/vs265/PDP.pdf An Introduction to Linear Algebra in Parallel Distributed Processing] in McClelland and Rumelhart, ''Parallel Distributed Processing'', MIT Press, 1985. | ||
* [http://redwood.berkeley.edu/ | * [http://redwood.berkeley.edu/vs265/linear-neuron/linear-neuron-models.html Linear neuron models] | ||
* [http://redwood.berkeley.edu/ | * [http://redwood.berkeley.edu/vs265/linear-algebra/linear-algebra.html Linear algebra primer] | ||
==== 07 Sep ==== | |||
* [http://redwood.berkeley.edu/vs265/superlearn1.pdf Handout] on supervised learning in single-stage feedforward networks | |||
* [http://redwood.berkeley.edu/vs265/superlearn2.pdf Handout] on supervised learning in multi-layer feedforward networks - "backpropagation" | |||
* Y. LeCun, L. Bottou, G. Orr, and K. Muller (1998) [http://redwood.berkeley.edu/vs265/lecun-98b.pdf "Efficient BackProp,"] in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.). | |||
* [http://www.cnl.salk.edu/ParallelNetsPronounce/index.php NetTalk demo] | |||
Revision as of 22:14, 7 September 2010
26 Aug
- Dreyfus, H.L. and Dreyfus, S.E. Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint. Daedalus, Winter 1988.
- Bell, A.J. Levels and loops: the future of artificial intelligence and neuroscience. Phil Trans: Bio Sci. 354:2013--2020 (1999) here or here
Optional:
- Land, MF and Fernald, RD. The Evolution of Eyes, Ann Revs Neuro, 1992.
31 Aug
- Mead, C. Chapter 1: Introduction and Chapter 4: Neurons from Analog VLSI and Neural Systems, Addison-Wesley, 1989.
- Linear time-invariant systems and convolution
- Simulating differential equations
- Dynamics
- Carandini M, Heeger D (1994) Summation and division by neurons in primate visual cortex. Science, 264: 1333-1336.
02 Sep
- Jordan, M.I. An Introduction to Linear Algebra in Parallel Distributed Processing in McClelland and Rumelhart, Parallel Distributed Processing, MIT Press, 1985.
- Linear neuron models
- Linear algebra primer
07 Sep
- Handout on supervised learning in single-stage feedforward networks
- Handout on supervised learning in multi-layer feedforward networks - "backpropagation"
- Y. LeCun, L. Bottou, G. Orr, and K. Muller (1998) "Efficient BackProp," in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.).
- NetTalk demo
