6. Unsupervised Learning

Unit 6 1 Unsupervised Learning

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Unit 6 1a Answer

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Unit 6 1b Question

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Unit 6 1c Answer

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Unit 6 2 Terminology

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Unit 6 3 Google Street View and Clustering

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Unit 6 4 k-Means Clustering Example

The probability here is "proportional", not equal.

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Unit 6 4a k-Means Algorithm

For nearest distance, you may assume Euclidean.  However, you can also generally substitute other distance metrics.

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Unit 6 4b Answer

For nearest distance, you may assume Euclidean.  However, you can also generally substitute other distance metrics.

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Unit 6 4c Question

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Unit 6 4d Answer

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Unit 6 4e Question

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Unit 6 4f Answer

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Unit 6 5 Expectation Maximization

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Unit 6 5a Gaussian Learning

The formula is missing a minus sign: f ( x,mu,sigma^2 ) = 1 / ( sqrt (2 * pi * sigma^2) * exp ( -(x-mu)^2 / ( 2 * sigma^2 )

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Unit 6 5b Maximum Likelihood

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Unit 6 5c Answer

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Unit 6 5d Question

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Unit 6 5e Answer

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Unit 6 5f Question

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Unit 6 5g Answer

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Unit 6 5h Gaussian Summary

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Unit 6 5i EM as Generalization of k-Means

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Unit 6 5j EM Algorithm

I forgot the normalizer in the calculation of the expectations e_ij. Sorry. As it stands, the expectations are "proportional" and not "equal"

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Unit 6 5k Answer

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Unit 6 5l Question

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Unit 6 5m Answer

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Unit 6 5n Question

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Unit 6 5o Answer

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Unit 6 5p Choosing k

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Unit 6 5q Clustering Summary

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Unit 6 6 Dimensionality Reduction

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Unit 6 6a Answer

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Unit 6 6b Question

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Unit 6 6c Answer

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Unit 6 6d Linear Dimensionality Reduction

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Unit 6 6e Face Example

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Unit 6 6f Scan Example

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Unit 6 6g Piece-Wise Linear Projection

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Unit 6 7 Spectral Clustering

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Unit 6 7a Answer

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Unit 6 7b Spectral Clustering Algorithm

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Unit 6 7c Answer

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Unit 6 7d Question

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Unit 6 7e Answer

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Unit 6 8 Supervised vs Unsupervised Learning

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