update cites, titles, regul2plots

Signed-off-by: Ziyu-Mu <mu.ziyu.ovo@gmail.com>
slds-lmu · May 27, 2024 · 6b813e3 · 6b813e3
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diff --git a/slides/R.mk b/slides/R.mk
diff --git a/slides/README.md b/slides/README.md
diff --git a/slides/advriskmin/Makefile b/slides/advriskmin/Makefile
diff --git a/slides/advriskmin/chapter-order.tex b/slides/advriskmin/chapter-order.tex
diff --git a/slides/advriskmin/figure/L2-loss.png b/slides/advriskmin/figure/L2-loss.png
diff --git a/slides/advriskmin/figure/bayes_error_5.png b/slides/advriskmin/figure/bayes_error_5.png
diff --git a/slides/advriskmin/figure/bias_variance_decomposition-bootstrap_1.png b/slides/advriskmin/figure/bias_variance_decomposition-bootstrap_1.png
diff --git a/slides/advriskmin/figure/bias_variance_decomposition-bootstrap_2.png b/slides/advriskmin/figure/bias_variance_decomposition-bootstrap_2.png
diff --git a/slides/advriskmin/figure/bias_variance_decomposition-complex_model.png b/slides/advriskmin/figure/bias_variance_decomposition-complex_model.png
diff --git a/slides/advriskmin/figure/bias_variance_decomposition-complex_model_bias.png b/slides/advriskmin/figure/bias_variance_decomposition-complex_model_bias.png
diff --git a/slides/advriskmin/figure/bias_variance_decomposition-complex_model_variance.png b/slides/advriskmin/figure/bias_variance_decomposition-complex_model_variance.png
diff --git a/slides/advriskmin/figure/bias_variance_decomposition-correct_model.png b/slides/advriskmin/figure/bias_variance_decomposition-correct_model.png
diff --git a/slides/advriskmin/figure/bias_variance_decomposition-correct_model_bias.png b/slides/advriskmin/figure/bias_variance_decomposition-correct_model_bias.png
diff --git a/slides/advriskmin/figure/bias_variance_decomposition-correct_model_variance.png b/slides/advriskmin/figure/bias_variance_decomposition-correct_model_variance.png
diff --git a/slides/advriskmin/figure/bias_variance_decomposition-linear_model.png b/slides/advriskmin/figure/bias_variance_decomposition-linear_model.png
diff --git a/slides/advriskmin/figure/bias_variance_decomposition-linear_model_bias.png b/slides/advriskmin/figure/bias_variance_decomposition-linear_model_bias.png
diff --git a/slides/advriskmin/figure/bias_variance_decomposition-linear_model_variance.png b/slides/advriskmin/figure/bias_variance_decomposition-linear_model_variance.png
diff --git a/slides/advriskmin/figure/bias_variance_decomposition-train_test.png b/slides/advriskmin/figure/bias_variance_decomposition-train_test.png
diff --git a/slides/advriskmin/figure/ftrue.pdf b/slides/advriskmin/figure/ftrue.pdf
diff --git a/slides/advriskmin/figure/ftrue.png b/slides/advriskmin/figure/ftrue.png
diff --git a/slides/advriskmin/figure/l1_vs_l2.png b/slides/advriskmin/figure/l1_vs_l2.png
diff --git a/slides/advriskmin/figure/laplace_plot.pdf b/slides/advriskmin/figure/laplace_plot.pdf
diff --git a/slides/advriskmin/figure/lasso_penalized.png b/slides/advriskmin/figure/lasso_penalized.png
diff --git a/slides/advriskmin/figure/lasso_penalty.png b/slides/advriskmin/figure/lasso_penalty.png
diff --git a/slides/advriskmin/figure/lasso_unpenalized.png b/slides/advriskmin/figure/lasso_unpenalized.png
diff --git a/slides/advriskmin/figure/log_reg_erm.pdf b/slides/advriskmin/figure/log_reg_erm.pdf
diff --git a/slides/advriskmin/figure/log_reg_ml.pdf b/slides/advriskmin/figure/log_reg_ml.pdf
diff --git a/slides/advriskmin/figure/loss_absolute_1.png b/slides/advriskmin/figure/loss_absolute_1.png
diff --git a/slides/advriskmin/figure/loss_absolute_2.png b/slides/advriskmin/figure/loss_absolute_2.png
diff --git a/slides/advriskmin/figure/loss_cauchy.png b/slides/advriskmin/figure/loss_cauchy.png
diff --git a/slides/advriskmin/figure/loss_dist_based.png b/slides/advriskmin/figure/loss_dist_based.png
diff --git a/slides/advriskmin/figure/loss_eps_insensitive.png b/slides/advriskmin/figure/loss_eps_insensitive.png
diff --git a/slides/advriskmin/figure/loss_huber_plot.png b/slides/advriskmin/figure/loss_huber_plot.png
diff --git a/slides/advriskmin/figure/loss_l1_l2.png b/slides/advriskmin/figure/loss_l1_l2.png
diff --git a/slides/advriskmin/figure/loss_logbarrier_1.png b/slides/advriskmin/figure/loss_logbarrier_1.png
diff --git a/slides/advriskmin/figure/loss_logbarrier_2.png b/slides/advriskmin/figure/loss_logbarrier_2.png
diff --git a/slides/advriskmin/figure/loss_logcosh.png b/slides/advriskmin/figure/loss_logcosh.png
diff --git a/slides/advriskmin/figure/loss_quadratic_1.png b/slides/advriskmin/figure/loss_quadratic_1.png
diff --git a/slides/advriskmin/figure/loss_quadratic_2.png b/slides/advriskmin/figure/loss_quadratic_2.png
diff --git a/slides/advriskmin/figure/loss_quantile.png b/slides/advriskmin/figure/loss_quantile.png
diff --git a/slides/advriskmin/figure/loss_symmetric.png b/slides/advriskmin/figure/loss_symmetric.png
diff --git a/slides/advriskmin/figure/loss_transl_inv.png b/slides/advriskmin/figure/loss_transl_inv.png
diff --git a/slides/advriskmin/figure/optimal_pointwise.png b/slides/advriskmin/figure/optimal_pointwise.png
diff --git a/slides/advriskmin/figure/optimal_pointwise_1.png b/slides/advriskmin/figure/optimal_pointwise_1.png
diff --git a/slides/advriskmin/figure/optimal_pointwise_2.png b/slides/advriskmin/figure/optimal_pointwise_2.png
diff --git a/slides/advriskmin/figure/plot_bernoulli.png b/slides/advriskmin/figure/plot_bernoulli.png
diff --git a/slides/advriskmin/figure/plot_bernoulli_plusmin_encoding.png b/slides/advriskmin/figure/plot_bernoulli_plusmin_encoding.png
diff --git a/slides/advriskmin/figure/plot_bernoulli_prob.png b/slides/advriskmin/figure/plot_bernoulli_prob.png
diff --git a/slides/advriskmin/figure/plot_brier.png b/slides/advriskmin/figure/plot_brier.png
diff --git a/slides/advriskmin/figure/plot_loss_01.png b/slides/advriskmin/figure/plot_loss_01.png
diff --git a/slides/advriskmin/figure/plot_loss_exponential.png b/slides/advriskmin/figure/plot_loss_exponential.png
diff --git a/slides/advriskmin/figure/plot_loss_hinge.png b/slides/advriskmin/figure/plot_loss_hinge.png
diff --git a/slides/advriskmin/figure/plot_loss_hinge_squared.png b/slides/advriskmin/figure/plot_loss_hinge_squared.png
diff --git a/slides/advriskmin/figure/plot_loss_overview.png b/slides/advriskmin/figure/plot_loss_overview.png
diff --git a/slides/advriskmin/figure/plot_loss_overview_classif.png b/slides/advriskmin/figure/plot_loss_overview_classif.png
diff --git a/slides/advriskmin/figure/plot_loss_overview_classif_2.png b/slides/advriskmin/figure/plot_loss_overview_classif_2.png
diff --git a/slides/advriskmin/figure/plot_loss_squared_scores.png b/slides/advriskmin/figure/plot_loss_squared_scores.png
diff --git a/slides/advriskmin/figure/plot_quad_pseudores.png b/slides/advriskmin/figure/plot_quad_pseudores.png
diff --git a/slides/advriskmin/figure/pseudo_residual_1.png b/slides/advriskmin/figure/pseudo_residual_1.png
diff --git a/slides/advriskmin/figure/pseudo_residual_2.png b/slides/advriskmin/figure/pseudo_residual_2.png
diff --git a/slides/advriskmin/figure/residuals_plot_Huber_L1.pdf b/slides/advriskmin/figure/residuals_plot_Huber_L1.pdf
diff --git a/slides/advriskmin/figure/residuals_plot_L1.pdf b/slides/advriskmin/figure/residuals_plot_L1.pdf
diff --git a/slides/advriskmin/figure/residuals_plot_L1_title.png b/slides/advriskmin/figure/residuals_plot_L1_title.png
diff --git a/slides/advriskmin/figure/residuals_plot_L2.pdf b/slides/advriskmin/figure/residuals_plot_L2.pdf
diff --git a/slides/advriskmin/figure/residuals_plot_L2_title.png b/slides/advriskmin/figure/residuals_plot_L2_title.png
diff --git a/slides/advriskmin/figure/robustness.png b/slides/advriskmin/figure/robustness.png
diff --git a/slides/advriskmin/figure/softmax_1.png b/slides/advriskmin/figure/softmax_1.png
diff --git a/slides/advriskmin/figure/softmax_2.png b/slides/advriskmin/figure/softmax_2.png
diff --git a/slides/advriskmin/figure_man/S_function_plot.pdf b/slides/advriskmin/figure_man/S_function_plot.pdf
diff --git a/slides/advriskmin/figure_man/convex-vs-nonconfex-landscape.png b/slides/advriskmin/figure_man/convex-vs-nonconfex-landscape.png
diff --git a/slides/advriskmin/figure_man/cosh-gaussian-densities.png b/slides/advriskmin/figure_man/cosh-gaussian-densities.png
diff --git a/slides/advriskmin/figure_man/lcurve_1.png b/slides/advriskmin/figure_man/lcurve_1.png
diff --git a/slides/advriskmin/figure_man/logcosh-derivation.png b/slides/advriskmin/figure_man/logcosh-derivation.png
diff --git a/slides/advriskmin/figure_man/optimization_steps.jpeg b/slides/advriskmin/figure_man/optimization_steps.jpeg
diff --git a/slides/advriskmin/figure_man/plot_const_var.png b/slides/advriskmin/figure_man/plot_const_var.png
diff --git a/slides/advriskmin/figure_man/risk_minimization_diagram.png b/slides/advriskmin/figure_man/risk_minimization_diagram.png
diff --git a/slides/advriskmin/figure_man/vgg_example.png b/slides/advriskmin/figure_man/vgg_example.png
diff --git a/slides/advriskmin/references.bib b/slides/advriskmin/references.bib
@@ -0,0 +1,29 @@
+@inproceedings{SOLLICH1999NINTH,
+  author={Sollich, P.},
+  booktitle={1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470)}, 
+  title={Probabilistic interpretations and Bayesian methods for support vector machines}, 
+  year={1999},
+  volume={1},
+  number={},
+  pages={91-96 vol.1},
+  keywords={},
+  doi={10.1049/cp:19991090},
+  url={https://ieeexplore.ieee.org/abstract/document/819547}
+}
+
+@inproceedings{MEYER2021ALTERNATIVE,
+  title={An alternative probabilistic interpretation of the huber loss},
+  author={Meyer, Gregory P},
+  booktitle={Proceedings of the ieee/cvf conference on computer vision and pattern recognition},
+  pages={5261--5269},
+  year={2021},
+  url={https://openaccess.thecvf.com/content/CVPR2021/papers/Meyer_An_Alternative_Probabilistic_Interpretation_of_the_Huber_Loss_CVPR_2021_paper.pdf}
+}
+
+@article{SALEH2022STATISTICAL,
+  title={Statistical properties of the log-cosh loss function used in machine learning},
+  author={Saleh, Resve A and Saleh, AK},
+  journal={arXiv preprint arXiv:2208.04564},
+  year={2022},
+  url={https://arxiv.org/pdf/2208.04564}
+}
diff --git a/slides/advriskmin/rsrc/S_function_plot.nb b/slides/advriskmin/rsrc/S_function_plot.nb
diff --git a/slides/advriskmin/rsrc/bias_variance_decomposition.R b/slides/advriskmin/rsrc/bias_variance_decomposition.R
diff --git a/slides/advriskmin/rsrc/error_surface.R b/slides/advriskmin/rsrc/error_surface.R
diff --git a/slides/advriskmin/rsrc/example_intro.R b/slides/advriskmin/rsrc/example_intro.R
diff --git a/slides/advriskmin/rsrc/ggsave-4.R b/slides/advriskmin/rsrc/ggsave-4.R
diff --git a/slides/advriskmin/rsrc/helpers/loss_functions.R b/slides/advriskmin/rsrc/helpers/loss_functions.R
diff --git a/slides/advriskmin/rsrc/lasso.R b/slides/advriskmin/rsrc/lasso.R
diff --git a/slides/advriskmin/rsrc/logcosh-derivation.R b/slides/advriskmin/rsrc/logcosh-derivation.R
diff --git a/slides/advriskmin/rsrc/loss_properties.R b/slides/advriskmin/rsrc/loss_properties.R
diff --git a/slides/advriskmin/rsrc/make_bayes_error_plots.R b/slides/advriskmin/rsrc/make_bayes_error_plots.R
diff --git a/slides/advriskmin/rsrc/make_different_losses_plots.R b/slides/advriskmin/rsrc/make_different_losses_plots.R
diff --git a/slides/advriskmin/rsrc/make_empirical_risk_plots.R b/slides/advriskmin/rsrc/make_empirical_risk_plots.R
diff --git a/slides/advriskmin/rsrc/make_graddesc_plot.R b/slides/advriskmin/rsrc/make_graddesc_plot.R
diff --git a/slides/advriskmin/rsrc/make_logreg_ml_erm_plot.R b/slides/advriskmin/rsrc/make_logreg_ml_erm_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_absolute_plot.R b/slides/advriskmin/rsrc/make_loss_absolute_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_bernoulli_plots.R b/slides/advriskmin/rsrc/make_loss_bernoulli_plots.R
diff --git a/slides/advriskmin/rsrc/make_loss_brier_plot.R b/slides/advriskmin/rsrc/make_loss_brier_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_cauchy_plot.R b/slides/advriskmin/rsrc/make_loss_cauchy_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_cauchy_plot1.R b/slides/advriskmin/rsrc/make_loss_cauchy_plot1.R
diff --git a/slides/advriskmin/rsrc/make_loss_eps_insensitive_plot.R b/slides/advriskmin/rsrc/make_loss_eps_insensitive_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_exponential_plot.R b/slides/advriskmin/rsrc/make_loss_exponential_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_hinge_plot.R b/slides/advriskmin/rsrc/make_loss_hinge_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_huber_plot.R b/slides/advriskmin/rsrc/make_loss_huber_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_logbarrier_plot.R b/slides/advriskmin/rsrc/make_loss_logbarrier_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_logcosh_plot.R b/slides/advriskmin/rsrc/make_loss_logcosh_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_overview_classif_plot.R b/slides/advriskmin/rsrc/make_loss_overview_classif_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_overview_plot.R b/slides/advriskmin/rsrc/make_loss_overview_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_quad_pseudores_plot.R b/slides/advriskmin/rsrc/make_loss_quad_pseudores_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_quadratic_plot.R b/slides/advriskmin/rsrc/make_loss_quadratic_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_quantile_plot.R b/slides/advriskmin/rsrc/make_loss_quantile_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_squared_scores_plot.R b/slides/advriskmin/rsrc/make_loss_squared_scores_plot.R
diff --git a/slides/advriskmin/rsrc/make_loss_zero_one_plot.R b/slides/advriskmin/rsrc/make_loss_zero_one_plot.R
diff --git a/slides/advriskmin/rsrc/make_optimal_constant_model_plots.R b/slides/advriskmin/rsrc/make_optimal_constant_model_plots.R
diff --git a/slides/advriskmin/rsrc/make_optimal_pointwise_prediction_plots.R b/slides/advriskmin/rsrc/make_optimal_pointwise_prediction_plots.R
diff --git a/slides/advriskmin/rsrc/make_pseudo_residual_plots.R b/slides/advriskmin/rsrc/make_pseudo_residual_plots.R
diff --git a/slides/advriskmin/rsrc/make_residual_plots.R b/slides/advriskmin/rsrc/make_residual_plots.R
diff --git a/slides/advriskmin/rsrc/make_robustness_plot.R b/slides/advriskmin/rsrc/make_robustness_plot.R
diff --git a/slides/advriskmin/rsrc/optimal-constant-model-variance.R b/slides/advriskmin/rsrc/optimal-constant-model-variance.R
diff --git a/slides/advriskmin/rsrc/optimal_constant_model.R b/slides/advriskmin/rsrc/optimal_constant_model.R
diff --git a/slides/advriskmin/rsrc/softmax_fun.R b/slides/advriskmin/rsrc/softmax_fun.R
diff --git a/slides/advriskmin/rsrc/tangent_line_y_x2.R b/slides/advriskmin/rsrc/tangent_line_y_x2.R
diff --git a/slides/advriskmin/slides-advriskmin-bias-variance-decomposition-deepdive.tex b/slides/advriskmin/slides-advriskmin-bias-variance-decomposition-deepdive.tex
@@ -4,26 +4,24 @@
 \input{../../latex-math/basic-ml}
 \input{../../latex-math/ml-eval}
 
-\newcommand{\titlefigure}{figure/bias_variance_decomposition-linear_model_bias.png}
-\newcommand{\learninggoals}{
+\title{Introduction to Machine Learning}
+
+\begin{document}
+
+\titlemeta{% Chunk title (example: CART, Forests, Boosting, ...), can be empty
+    Advanced Risk Minimization
+  }{% Lecture title  
+    Bias-Variance Decomposition (Deep-Dive)
+  }{% Relative path to title page image: Can be empty but must not start with slides/
+  figure/bias_variance_decomposition-linear_model_bias.png
+  }{
   \item Understand how to decompose the generalization error of a learner into 
   \begin{itemize}
     \item \footnotesize Bias of the learner
     \item \footnotesize Variance of the learner
     \item \footnotesize Inherent noise in the data
   \end{itemize} 
-  }
-
-\title{Introduction to Machine Learning}
-\date{}
-
-\begin{document}
-
-\lecturechapter{Bias-Variance Decomposition (Deep-Dive)}
-\lecture{Introduction to Machine Learning}
-
-
-
+}
 
 \begin{vbframe} {Bias-Variance decomposition}
 

diff --git a/slides/advriskmin/slides-advriskmin-bias-variance-decomposition.tex b/slides/advriskmin/slides-advriskmin-bias-variance-decomposition.tex
@@ -4,27 +4,25 @@
 \input{../../latex-math/basic-ml}
 \input{../../latex-math/ml-eval}
 
-\newcommand{\titlefigure}{figure/bias_variance_decomposition-linear_model_bias.png}
-\newcommand{\learninggoals}{
+\title{Introduction to Machine Learning}
+
+\begin{document}
+
+\titlemeta{% Chunk title (example: CART, Forests, Boosting, ...), can be empty
+    Advanced Risk Minimization
+  }{% Lecture title  
+    Advanced Risk Minimization:\\
+    Bias-Variance Decomposition
+  }{% Relative path to title page image: Can be empty but must not start with slides/
+  figure/bias_variance_decomposition-linear_model_bias.png
+  }{
   \item Understand how to decompose the generalization error of a learner into 
   \begin{itemize}
     \item \footnotesize bias of the learner
     \item \footnotesize variance of the learner
     \item \footnotesize inherent noise in the data
   \end{itemize} 
-  }
-
-\title{Introduction to Machine Learning}
-\date{}
-
-\begin{document}
-
-\lecturechapter{Advanced Risk Minimization:\\
-Bias-Variance Decomposition}
-\lecture{Introduction to Machine Learning}
-
-
-
+}
 
 \begin{vbframe} {Bias-Variance decomposition}
 

diff --git a/slides/advriskmin/slides-advriskmin-classification-01.tex b/slides/advriskmin/slides-advriskmin-classification-01.tex
@@ -11,23 +11,20 @@
 \input{../../latex-math/basic-math}
 \input{../../latex-math/basic-ml}
 
-\newcommand{\titlefigure}{figure/plot_loss_01.png}
-\newcommand{\learninggoals}{
-  \item Derive the risk minimizer of the 0-1-loss
-  \item Derive the optimal constant model for the 0-1-loss
-}
-
 \title{Introduction to Machine Learning}
-% \author{Bernd Bischl, Christoph Molnar, Daniel Schalk, Fabian Scheipl}
-\institute{\href{https://compstat-lmu.github.io/lecture_i2ml/}{compstat-lmu.github.io/lecture\_i2ml}}
-\date{}
-
-
 
 \begin{document}
-
-\lecturechapter{0-1-Loss}
-\lecture{Introduction to Machine Learning}
+
+\titlemeta{% Chunk title (example: CART, Forests, Boosting, ...), can be empty
+    Advanced Risk Minimization
+  }{% Lecture title  
+    0-1-Loss
+  }{% Relative path to title page image: Can be empty but must not start with slides/
+  figure/plot_loss_01.png
+  }{
+  \item Derive the risk minimizer of the 0-1-loss
+  \item Derive the optimal constant model for the 0-1-loss
+}
 
 \begin{vbframe}{0-1-Loss}
 

diff --git a/slides/advriskmin/slides-advriskmin-classification-bernoulli.tex b/slides/advriskmin/slides-advriskmin-classification-bernoulli.tex
@@ -14,28 +14,23 @@
 \input{../../latex-math/ml-eval}
 \input{../../latex-math/ml-trees} % For the comparison of Brier and Gini index
 
-\newcommand{\titlefigure}{figure/plot_bernoulli_prob}
-\newcommand{\learninggoals}{
+\title{Introduction to Machine Learning}
+
+\begin{document}
+
+\titlemeta{% Chunk title (example: CART, Forests, Boosting, ...), can be empty
+    Advanced Risk Minimization
+  }{% Lecture title  
+    Bernoulli Loss
+  }{% Relative path to title page image: Can be empty but must not start with slides/
+  figure/plot_bernoulli_prob.png
+  }{
   \item Know the Bernoulli loss and related losses (log-loss, logistic loss, Binomial loss)
   \item Derive the risk minimizer
   \item Derive the optimal constant model 
   \item Understand the connection between log-loss and entropy splitting 
 }
 
-\title{Introduction to Machine Learning}
-% \author{Bernd Bischl, Christoph Molnar, Daniel Schalk, Fabian Scheipl}
-\institute{\href{https://compstat-lmu.github.io/lecture_i2ml/}{compstat-lmu.github.io/lecture\_i2ml}}
-\date{}
-
-
-
-\begin{document}
-
-\lecturechapter{Bernoulli Loss}
-\lecture{Introduction to Machine Learning}
-
-
-
 \begin{vbframe}{Bernoulli Loss}
 
 \vspace*{-0.5cm}

diff --git a/slides/advriskmin/slides-advriskmin-classification-brier.tex b/slides/advriskmin/slides-advriskmin-classification-brier.tex
@@ -13,27 +13,23 @@
 \input{../../latex-math/basic-ml}
 \input{../../latex-math/ml-trees} % For the comparison of Brier and Gini index
 
+\title{Introduction to Machine Learning}
 
-\newcommand{\titlefigure}{figure/plot_brier.png}
-\newcommand{\learninggoals}{
+\begin{document}
+
+\titlemeta{% Chunk title (example: CART, Forests, Boosting, ...), can be empty
+    Advanced Risk Minimization
+  }{% Lecture title  
+    Brier Score
+  }{% Relative path to title page image: Can be empty but must not start with slides/
+  figure/plot_brier.png
+  }{
   \item Know the Brier score 
   \item Derive the risk minimizer
   \item Derive the optimal constant model 
   \item Understand the connection between Brier score and Gini splitting 
 }
 
-\title{Introduction to Machine Learning}
-% \author{Bernd Bischl, Christoph Molnar, Daniel Schalk, Fabian Scheipl}
-\institute{\href{https://compstat-lmu.github.io/lecture_i2ml/}{compstat-lmu.github.io/lecture\_i2ml}}
-\date{}
-
-
-\begin{document}
-
-\lecturechapter{Brier Score}
-\lecture{Introduction to Machine Learning}
-
-
 % \begin{vbframe}{Classification Losses: (Naive) L2-Loss}
 
 

diff --git a/slides/advriskmin/slides-advriskmin-classification-deepdive.tex b/slides/advriskmin/slides-advriskmin-classification-deepdive.tex
@@ -11,25 +11,22 @@
 \input{../../latex-math/basic-math}
 \input{../../latex-math/basic-ml}
 
-\newcommand{\titlefigure}
-{figure/plot_bernoulli_prob}
-\newcommand{\learninggoals}{
-  \item Derive the optimal constant model for the binary empirical log loss risk
-  \item Derive the optimal constant model for the empirical multiclass log loss risk
-}
+\newcommand{\argminl}{\mathop{\operatorname{arg\,min}}\limits}
 
 \title{Introduction to Machine Learning}
-% \author{Bernd Bischl, Christoph Molnar, Daniel Schalk, Fabian Scheipl}
-\institute{\href{https://compstat-lmu.github.io/lecture_i2ml/}{compstat-lmu.github.io/lecture\_i2ml}}
-\date{}
-
-\newcommand{\argminl}{\mathop{\operatorname{arg\,min}}\limits}
 
 \begin{document}
-
-\lecturechapter{Optimal constant model for the empirical log loss risk (Deep-Dive)}
-\lecture{Introduction to Machine Learning}
-
+
+\titlemeta{% Chunk title (example: CART, Forests, Boosting, ...), can be empty
+    Advanced Risk Minimization
+  }{% Lecture title  
+    Optimal constant model for the empirical log loss risk (Deep-Dive)
+  }{% Relative path to title page image: Can be empty but must not start with slides/
+  figure/plot_bernoulli_prob.png
+  }{
+  \item Derive the optimal constant model for the binary empirical log loss risk
+  \item Derive the optimal constant model for the empirical multiclass log loss risk
+}
 
 \begin{vbframe}{Binary log loss: Emp. Risk Minimizer}
 

diff --git a/slides/advriskmin/slides-advriskmin-classification-furtherlosses.tex b/slides/advriskmin/slides-advriskmin-classification-furtherlosses.tex
@@ -14,26 +14,23 @@
 \input{../../latex-math/ml-eval}
 \input{../../latex-math/ml-trees} % For the comparison of Brier and Gini index
 
+\title{Introduction to Machine Learning}
 
-\newcommand{\titlefigure}{figure/plot_loss_overview_classif.png}
-\newcommand{\learninggoals}{
+\begin{document}
+
+\titlemeta{% Chunk title (example: CART, Forests, Boosting, ...), can be empty
+    Advanced Risk Minimization
+  }{% Lecture title  
+    Advanced Classification Losses
+  }{% Relative path to title page image: Can be empty but must not start with slides/
+  figure/plot_loss_overview_classif.png
+  }{
   \item Know the (squared) hinge loss
   \item Know the $L2$ loss defined on scores
   \item Know the exponential loss
   \item Know the AUC loss 
 }
 
-\title{Introduction to Machine Learning}
-% \author{Bernd Bischl, Christoph Molnar, Daniel Schalk, Fabian Scheipl}
-\institute{\href{https://compstat-lmu.github.io/lecture_i2ml/}{compstat-lmu.github.io/lecture\_i2ml}}
-\date{}
-
-
-\begin{document}
-
-\lecturechapter{Advanced Classification Losses}
-\lecture{Introduction to Machine Learning}
-
 \begin{vbframe}{Hinge Loss}
 
 \begin{itemize}

diff --git a/slides/advriskmin/slides-advriskmin-logreg-deepdive.tex b/slides/advriskmin/slides-advriskmin-logreg-deepdive.tex
@@ -11,26 +11,23 @@
 \input{../../latex-math/basic-math}
 \input{../../latex-math/basic-ml}
 
-\newcommand{\titlefigure}
-{figure/plot_bernoulli_prob}
-\newcommand{\learninggoals}{
-  \item Derive the gradient of the logistic regression
-  \item Derive the Hessian of the logistic regression
-  \item Show that the logistic regression is a convex problem
-}
+\newcommand{\argminl}{\mathop{\operatorname{arg\,min}}\limits}
 
 \title{Introduction to Machine Learning}
-% \author{Bernd Bischl, Christoph Molnar, Daniel Schalk, Fabian Scheipl}
-\institute{\href{https://compstat-lmu.github.io/lecture_i2ml/}{compstat-lmu.github.io/lecture\_i2ml}}
-\date{}
-
-\newcommand{\argminl}{\mathop{\operatorname{arg\,min}}\limits}
 
 \begin{document}
-
-\lecturechapter{Logistic regression (Deep-Dive)}
-\lecture{Introduction to Machine Learning}
-
+
+\titlemeta{% Chunk title (example: CART, Forests, Boosting, ...), can be empty
+    Advanced Risk Minimization
+  }{% Lecture title  
+    Logistic regression (Deep-Dive)
+  }{% Relative path to title page image: Can be empty but must not start with slides/
+  figure/plot_bernoulli_prob.png
+  }{
+  \item Derive the gradient of the logistic regression
+  \item Derive the Hessian of the logistic regression
+  \item Show that the logistic regression is a convex problem
+}
 
 \begin{vbframe}{Logistic regression: Risk Problem}
 

diff --git a/slides/advriskmin/slides-advriskmin-losses-properties.tex b/slides/advriskmin/slides-advriskmin-losses-properties.tex
@@ -15,29 +15,24 @@
 
 \usepackage{booktabs}
 
-\newcommand{\titlefigure}{figure_man/vgg_example.png}
-\newcommand{\learninggoals}{
+\title{Introduction to Machine Learning}
+
+\begin{document}
+
+\titlemeta{% Chunk title (example: CART, Forests, Boosting, ...), can be empty
+    Advanced Risk Minimization
+  }{% Lecture title  
+    Properties of Loss Functions
+  }{% Relative path to title page image: Can be empty but must not start with slides/
+  figure_man/vgg_example.png
+  }{
   % \item Understand why you should care about properties of loss functions
   \item Statistical properties 
   \item Robustness 
   \item Numerical properties
   \item Some fundamental terminology
-
 }
 
-\title{Introduction to Machine Learning}
-% \author{Bernd Bi{}schl, Christoph Molnar, Daniel Schalk, Fabian Scheipl}
-\institute{\href{https://compstat-lmu.github.io/lecture_i2ml/}{compstat-lmu.github.io/lecture\_i2ml}}
-\date{}
-
-
-\begin{document}
-
-% ------------------------------------------------------------------------------
-
-\lecturechapter{Properties of Loss Functions}
-\lecture{Introduction to Machine Learning}
-
 \begin{vbframe}{The role of Loss Functions}
 
 Why should we care about the choice of the loss function $\Lxy$?