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@ -3,7 +3,10 @@
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SRCS := $(wildcard *.md)
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PDF := $(SRCS:%.md=%.pdf)
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OPT := --pdf-engine=xelatex --variable mainfont="Helvetica" --variable sansfont="Helvetica" -t beamer -s -fmarkdown-implicit_figures --template=template.beamer --highlight-style=kate
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#OPT := --pdf-engine=xelatex --variable mainfont="Helvetica" --variable sansfont="Helvetica" -t beamer -s -fmarkdown-implicit_figures --template=template.beamer --highlight-style=kate
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OPT := -t beamer -s -fmarkdown-implicit_figures --template=template.beamer --highlight-style=kate
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all: ${PDF}
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%.pdf: %.md
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@ -523,7 +523,7 @@ $$ R = |\vec x - \vec y|$$
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Better: take correlations between variables into account:
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$$ R = \sqrt{(\vec{x}-\vec{y})^T \mat{V}^{-1} (\vec{x}-\vec{y})} $$
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$$R = \sqrt{(\vec{x}-\vec{y})^T \mat{V}^{-1} (\vec{x}-\vec{y})}$$
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$$ \mat{V} = \text{covariance matrix}, R = \text{"Mahalanobis distance"}$$
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@ -933,7 +933,7 @@ Iris flower data set
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\vspace{2ex}
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\footnotesize
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[\textcolor{gray}{03\_ml\_basics\_iris\_softmax\_regression.ipynb}](https://nbviewer.jupyter.org/urls/www.physi.uni-heidelberg.de/~reygers/lectures/2022/ml/examples/03_ml_basics_iris_softmax_regression.ipynb)
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[\textcolor{gray}{03\_ml\_basics\_iris\_softmax\_regression.ipynb}](https://nbviewer.jupyter.org/urls/www.physi.uni-heidelberg.de/~reygers/lectures/2023/ml/examples/03_ml_basics_iris_softmax_regression.ipynb)
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\vspace{19ex}
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