VS298 (Fall 06): Schedule - Revision history

Bruno: /* Schedule */

2006-11-17T05:24:58Z

Schedule

← Older revision		Revision as of 05:16, 17 November 2006
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	*'''Week 10 (Nov. 8, 10)''': Probabilistic models and inference; The mixture of Gaussians model		*'''Week 10 (Nov. 8, 10)''': Probabilistic models and inference; The mixture of Gaussians model

	*'''Week 11 (Nov. 15~~, 17~~)''': Boltzmann machines		*'''Week 11 (Nov. 15)''': Boltzmann machines

	*'''Week 12 (Nov. 22~~, 24~~)''': Sparse coding and ICA		*'''Week 12 (Nov. 22)''': Sparse coding and ICA

	*'''Week 13 (Nov. 29, Dec. 1)''': Neural implementations; Integrate-and-fire model; Neural encoding and decoding; Limits of precision in neurons		*'''Week 13 (Nov. 29, Dec. 1)''': Neural implementations; Integrate-and-fire model; Neural encoding and decoding; Limits of precision in neurons

	*'''Week 14 (Dec. 6, 8)''': Special topics; student projects		*'''Week 14 (Dec. 6, 8)''': Special topics; student projects

Bruno: /* Schedule */

2006-11-05T23:00:05Z

Schedule

← Older revision		Revision as of 22:52, 5 November 2006
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	*'''Week 8 (Oct. 25, 27)''': Recurrent networks; attractor dynamics; Hopfield networks, Pattern completion; Line attractors and `bump circuits’		*'''Week 8 (Oct. 25, 27)''': Recurrent networks; attractor dynamics; Hopfield networks, Pattern completion; Line attractors and `bump circuits’

	*'''Week 9 (Nov. 1, 3)''': Associative memory models		*'''Week 9 (Nov. 1, 3)''': Associative memory models (Sommer, Kanerva)

	*'''Week 10 (Nov. 8, 10)''': Probabilistic models and inference; The mixture of Gaussians model		*'''Week 10 (Nov. 8, 10)''': Probabilistic models and inference; The mixture of Gaussians model

Bruno: /* Schedule */

2006-09-09T04:50:06Z

Schedule

← Older revision		Revision as of 04:42, 9 September 2006
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	*'''Week 1 (Sept. 6,8)''': Introduction; theory and modeling in neuroscience; linear neuron models; perceptron		*'''Week 1 (Sept. 6,8)''': Introduction; theory and modeling in neuroscience; linear neuron models; perceptron

	*'''Week 2 (Sept. 13,15)''': guest lecture (Bell) ~~on Wednesday, no class Friday~~		*'''Week 2 (Sept. 13,15)''': guest lecture (Bell, Wednesday only)

	*'''Week 3 (Sept. 20, 22)''': Perceptron learning rule; Widrow-Hoff rule; Objective functions and gradient descent; Multilayer networks and backpropagation		*'''Week 3 (Sept. 20, 22)''': Perceptron learning rule; Widrow-Hoff rule; Objective functions and gradient descent; Multilayer networks and backpropagation
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	*'''Week 4 (Sept. 27, 29)''': Reinforcement learning; Theory of associative reward-penalty; Models and critics		*'''Week 4 (Sept. 27, 29)''': Reinforcement learning; Theory of associative reward-penalty; Models and critics

	*'''Week 5 (Oct. 4, 6)''': guest lecture (Russell) ~~on Wednesday, no class Friday~~		*'''Week 5 (Oct. 4, 6)''': guest lecture (Russell, Wednesday only)

	*'''Week 6 (Oct. 11, 13)''': Unsupervised learning; Hebbian learning and PCA; winner-take-all networks and clustering; sparse, distributed coding		*'''Week 6 (Oct. 11, 13)''': Unsupervised learning; Hebbian learning and PCA; winner-take-all networks and clustering; sparse, distributed coding

Bruno: /* Schedule */

2006-09-09T04:48:38Z

Schedule

New page

== Schedule ==

*'''Week 1 (Sept. 6,8)''': Introduction; theory and modeling in neuroscience; linear neuron models; perceptron

*'''Week 2 (Sept. 13,15)''': guest lecture (Bell) on Wednesday, no class Friday

*'''Week 3 (Sept. 20, 22)''': Perceptron learning rule; Widrow-Hoff rule; Objective functions and gradient descent; Multilayer networks and backpropagation

*'''Week 4 (Sept. 27, 29)''': Reinforcement learning; Theory of associative reward-penalty; Models and critics

*'''Week 5 (Oct. 4, 6)''': guest lecture (Russell) on Wednesday, no class Friday

*'''Week 6 (Oct. 11, 13)''': Unsupervised learning; Hebbian learning and PCA; winner-take-all networks and clustering; sparse, distributed coding

*'''Week 7 (Oct. 18, 20)''': Plasticity and cortical maps; Self-organizing maps; Kohonen nets; Models of experience dependent learning and cortical reorganization

*'''Week 8 (Oct. 25, 27)''': Recurrent networks; attractor dynamics; Hopfield networks, Pattern completion; Line attractors and `bump circuits’

*'''Week 9 (Nov. 1, 3)''': Associative memory models

*'''Week 10 (Nov. 8, 10)''': Probabilistic models and inference; The mixture of Gaussians model

*'''Week 11 (Nov. 15, 17)''': Boltzmann machines

*'''Week 12 (Nov. 22, 24)''': Sparse coding and ICA

*'''Week 13 (Nov. 29, Dec. 1)''': Neural implementations; Integrate-and-fire model; Neural encoding and decoding; Limits of precision in neurons

*'''Week 14 (Dec. 6, 8)''': Special topics; student projects

Bruno at 07:23, 6 September 2006

2006-09-06T07:23:45Z

New page

== Schedule ==

Week 1 (Sept. 6,8): Introduction; theory and modeling in neuroscience; linear neuron models; perceptron

Week 2 (Sept. 13,15): guest lecture

Week 3 (Sept. 20, 22): Perceptron learning rule; Widrow-Hoff rule; Objective functions and gradient descent; Multilayer networks and backpropagation

Week 4 (Sept. 27, 29): Reinforcement learning; Theory of associative reward-penalty; Models and critics

Week 5 (Oct. 4, 6): guest lecture

Week 6 (Oct. 11, 13): Unsupervised learning; Hebbian learning and PCA; winner-take-all networks and clustering; sparse, distributed coding

Week 7 (Oct. 18, 20): Plasticity and cortical maps; Self-organizing maps; Kohonen nets; Models of experience dependent learning and cortical reorganization

Week 8 (Oct. 25, 27): Recurrent networks; attractor dynamics; Hopfield networks, Pattern completion; Line attractors and `bump circuits’

Week 9 (Nov. 1, 3): Associative memory models

Week 10 (Nov. 8, 10): Probabilistic models and inference; The mixture of Gaussians model

Week 11 (Nov. 15, 17): Boltzmann machines

Week 12 (Nov. 22, 24): Sparse coding and ICA

Week 13 (Nov. 29, Dec. 1): Neural implementations; Integrate-and-fire model; Neural encoding and decoding; Limits of precision in neurons

Week 14 (Dec. 6, 8): Special topics; student projects