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        <title>Introduction to Nature Inspired Computation networks</title>
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        <title>Introduction to Nature Inspired Computation</title>
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        <dc:date>2007-11-30T19:44:37+00:00</dc:date>
        <title>networks:long_tails</title>
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        <description>Lecture on 29.11.2007

[Presentation]</description>
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        <title>networks:mini-project_ideas</title>
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        <description>*  Present to the class some cutting-edge research on the brain and how it works
	*  Train a neural network to recognize handwritten Sütterlin (ask your grandparents what this is =))
	*  For some large dataset, train a neural network and do some analysis on model parameters such as number of hidden nodes, learning rate, momentum, etc. 
	*  Perform some deep network analysis on the Pleonast user graph (which I will provide)
	*  Do a Milgram-esque social experiment testing the Small Worlds hypoth…</description>
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        <title>networks:neural_networks</title>
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        <description>Lecture on 15.11.2007

[Presentation]

Neuron Activation

	*  Many neuron axons are attached to another neuron
	*  Neuron “sums” the activation of all inputs
	*  If the input is above a threshold, the neuron fires

 

Perceptrons

	*  Sum of inputs*weights, threshold function
	*  could learn AND &amp; OR but NOT XOR
	*  Only linearly separable functions</description>
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        <title>networks:small_worlds</title>
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        <description>Lecture on 22.11.2007

[Presentation]</description>
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        <dc:date>2007-12-13T15:07:27+00:00</dc:date>
        <title>networks:the_brain</title>
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        <description>Lecture on 8.11.2007

[Presentation]

This lecture was a short introduction to the biological brain, establishing some concepts that will be used in artificial neural networks.

Links

	*  Presentation about “The Brain” by Martin and Björn
	*  Article about researchers using a biological neural network to fly a flight simulator</description>
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