update

slides/Makefile

@@ -3,7 +3,10 @@
 SRCS := $(wildcard *.md)
 PDF := $(SRCS:%.md=%.pdf)
 
-OPT := --pdf-engine=xelatex --variable mainfont="Helvetica" --variable sansfont="Helvetica" -t beamer -s -fmarkdown-implicit_figures --template=template.beamer --highlight-style=kate 
+#OPT := --pdf-engine=xelatex --variable mainfont="Helvetica" --variable sansfont="Helvetica" -t beamer -s -fmarkdown-implicit_figures --template=template.beamer --highlight-style=kate
+OPT := -t beamer -s -fmarkdown-implicit_figures --template=template.beamer --highlight-style=kate
+
+
 all: ${PDF}
 
 %.pdf: %.md

slides/ml_basics.md

@@ -523,7 +523,7 @@ $$ R = |\vec x - \vec y|$$
 
 Better: take correlations between variables into account:
 
-$$ R = \sqrt{(\vec{x}-\vec{y})^T \mat{V}^{-1} (\vec{x}-\vec{y})} $$ 
+$$R = \sqrt{(\vec{x}-\vec{y})^T \mat{V}^{-1} (\vec{x}-\vec{y})}$$ 
 $$ \mat{V} = \text{covariance matrix}, R = \text{"Mahalanobis distance"}$$
 
 
@@ -933,7 +933,7 @@ Iris flower data set
 \vspace{2ex}
 
 \footnotesize 
-[\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)
+[\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)
 
 \vspace{19ex}