# VS298 (Fall 06): Reading: Difference between revisions

From RedwoodCenter

Jump to navigationJump to search
(→08 Sep) |
No edit summary |
||

(2 intermediate revisions by the same user not shown) | |||

Line 17: | Line 17: | ||

* [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"