VS265: Reading: Difference between revisions
From RedwoodCenter
Jump to navigationJump to search
| Line 20: | Line 20: | ||
==== 4 Sept: Linear neuron, Perceptron ==== | ==== 4 Sept: Linear neuron, Perceptron ==== | ||
* '''HKP''' chapter 6, '''DJCM''' chapters 38-40, 44, '''DA''' chapter 8 (sec. 4-6) | * '''HKP''' chapter 6, '''DJCM''' chapters 38-40, 44, '''DA''' chapter 8 (sec. 4-6) | ||
* [http://redwood.berkeley.edu/vs265/linear-neuron/linear-neuron-models.html Linear neuron models] | |||
* [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 | ||
* [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] | ||
* 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. | * 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. | ||
Revision as of 04:44, 1 September 2014
28 Aug: Introduction
- HKP chapter 1
- 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
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.
- O'Rourke, N.A et al. "Deep molecular diversity of mammalian synapses: why it matters and how to measure it." Nature Reviews Neurosci. 13, (2012)
2 Sept: Neuron models
- HKP chapter 5, DJCM chapters 38-40
- Mead, C. Chapter 1: Introduction and Chapter 4: Neurons from Analog VLSI and Neural Systems, Addison-Wesley, 1989.
- Dynamics
- Linear time-invariant systems and convolution
- Simulating differential equations
Optional
- Carandini M, Heeger D (1994) Summation and division by neurons in primate visual cortex. Science, 264: 1333-1336.
4 Sept: Linear neuron, Perceptron
- HKP chapter 6, DJCM chapters 38-40, 44, DA chapter 8 (sec. 4-6)
- Linear neuron models
- Handout on supervised learning in single-stage feedforward networks
- Linear algebra primer
- Jordan, M.I. An Introduction to Linear Algebra in Parallel Distributed Processing in McClelland and Rumelhart, Parallel Distributed Processing, MIT Press, 1985.
