VS298 (Fall 06): Reading: Difference between revisions
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* [http://redwood.berkeley.edu/~amir/vs298/linear-algebra/linear-algebra.html Linear algebra primer] | * [http://redwood.berkeley.edu/~amir/vs298/linear-algebra/linear-algebra.html Linear algebra primer] | ||
* [http://redwood.berkeley.edu/~amir/vs298/dynamics/dynamics.html Dynamics] | * [http://redwood.berkeley.edu/~amir/vs298/dynamics/dynamics.html Dynamics] | ||
==== 20 Sep ==== | |||
* [http://redwood.berkeley.edu/~amir/vs298/superlearn1.pdf Handout] on supervised learning in single-stage feedforward networks | |||
==== 22 Sep ==== | |||
* [http://redwood.berkeley.edu/~amir/vs298/superlearn2.pdf Handout] on supervised learning in multi-layer feedforward networks - "backpropagation" | |||
Revision as of 04:37, 21 September 2006
29 Aug
- Bell, A.J. Levels and loops: the future of artificial intelligence and neuroscience. Phil Trans: Bio Sci. 354:2013--2020 (1999) here or here
06 Sep
- Dreyfus, H.L. and Dreyfus, S.E. Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint. Daedalus, Winter 1988.
- Mead, C. Chapter 1: Introduction and Chapter 4: Neurons from Analog VLSI and Neural Systems, Addison-Wesley, 1989.
- Jordan, M.I. An Introduction to Linear Algebra in Parallel Distributed Processing in McClelland and Rumelhart, Parallel Distributed Processing, MIT Press, 1985.
- Zhang K, Sejnowski TJ (2000) A universal scaling law between gray matter and white matter of cerebral cortex. PNAS, 97: 5621–5626.
08 Sep
- Linear neuron models
- Linear time-invariant systems and convolution
- Simulating differential equations
- Carandini M, Heeger D (1994) Summation and division by neurons in primate visual cortex. Science, 264: 1333-1336.
Optional reading for more background:
20 Sep
- Handout on supervised learning in single-stage feedforward networks
22 Sep
- Handout on supervised learning in multi-layer feedforward networks - "backpropagation"
