# Difference between revisions of "VS265: Reading Fall2012"

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* [http://redwood.berkeley.edu/vs265/linear-algebra/linear-algebra.html Linear algebra primer] | * [http://redwood.berkeley.edu/vs265/linear-algebra/linear-algebra.html Linear algebra primer] | ||

* [http://redwood.berkeley.edu/vs265/superlearn_handout1.pdf Handout] on supervised learning in single-stage feedforward networks | * [http://redwood.berkeley.edu/vs265/superlearn_handout1.pdf Handout] on supervised learning in single-stage feedforward networks | ||

+ | |||

+ | ==== 17 Sep ==== | ||

+ | * [http://redwood.berkeley.edu/vs265/superlearn_handout2.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://cnl.salk.edu/Research/ParallelNetsPronounce/ NetTalk demo] |

## Revision as of 15:55, 17 September 2012

#### 27 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 - 1973 Lighthill debate on future of AI

#### 29 Aug

- Mead, C. Chapter 1: Introduction and Chapter 4: Neurons from
*Analog VLSI and Neural Systems*, Addison-Wesley, 1989. - Linear neuron models
- 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.

Optional:

- Land, MF and Fernald, RD. The Evolution of Eyes, Ann Revs Neuro, 1992.
- Zhang K, Sejnowski TJ (2000) A universal scaling law between gray matter and white matter of cerebral cortex. PNAS, 97: 5621–5626.

#### 05 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
- Handout on supervised learning in single-stage feedforward networks

#### 17 Sep

- 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