VS265: Reading Fall2010: Difference between revisions
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==== 21 Sep ==== | ==== 21 Sep ==== | ||
* Handout: [http://redwood.berkeley.edu/vs265/hebb-pca.pdf Hebbian learning and PCA] | * Handout: [http://redwood.berkeley.edu/vs265/hebb-pca-handout.pdf Hebbian learning and PCA] | ||
* '''HKP''' Chapters 8 and 9 | * '''HKP''' Chapters 8 and 9 | ||
* '''PDP''' [http://redwood.berkeley.edu/vs265/chap9.pdf Chapter 9] (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors) | * '''PDP''' [http://redwood.berkeley.edu/vs265/chap9.pdf Chapter 9] (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors) | ||
Revision as of 04:46, 23 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
21 Sep
- Handout: Hebbian learning and PCA
- HKP Chapters 8 and 9
- PDP Chapter 9 (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors)
Optional:
- Atick, Redlich. What does the retina know about natural scenes?, Neural Computation, 1992.
- Dan, Atick, Reid. Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory, J Neuroscience, 1996.
