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source-code/README.md
@@ -26,3 +26,7 @@ to create it. There is some material not covered in the presentation as well.
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examples are provided forsupervised (regression and classification) and
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unsupervised (clustering) learnign, as well as dimensionality reduction for
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visualization of high-demensional data.
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+* [`kullback_leibler_divergence.ipynb`}(kullback_leibler_divergence.ipynb): illustrating
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+ the Kullback-Leibler divergence for probability distributions.
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+* [`curse_of_dimensionality.ipynb`](curse_of_dimensionality.ipynb): illustration of
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+ the curse of dimensionality.
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