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Unsupervised Learning and Neural Networks

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Presentation on theme: "Unsupervised Learning and Neural Networks"— Presentation transcript:

1 Unsupervised Learning and Neural Networks
Sarah Aronow-Werner CogSci 110

2 Supervised vs. Unsupervised Learning
Supervised Learning: teaching a network to respond with the correct output when given an input. Unsupervised Learning: allowing the network manipulate input without a given ‘correct’ output.

3 What Can Unsupervised Learning Networks Accomplish?
Discover underlying structure of data Encode data Compress data Transform data

4 Autoassociative Networks
Goal: to find compressed representation of data. Computes identity function. Hidden layer has fewer nodes that input and output layers.

5

6 Linear Compression Non-Linear Compression

7 Competitive Networks Goal: to divide data into clusters with similar features. Can provide a single ‘prototype’ representative of cluster. User chooses the number of vectors to identify. Single winning output.

8 Kohonen’s Self-Organizing Map (SOM)
Goal: to discover underlying structure while preserving topology. Location of output node represents relationship to other outputs. Tries to mimic the organization of the brain.

9 Other Not-Fully-Supervised Networks
Reinforcement Learning Few outputs for a large number of inputs. Learning with a critic rather than a teacher Forecasting Delayed correct output Can be used for economic or weather forecasting

10 Questions or Comments?

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