lectures: first session structure

.gitignore

@@ -17,3 +17,7 @@ public/
 *.ppt*
 *.pdf
 *.doc*
+
+# PyCharm
+**.idea
+**.env

lectures_beamer/S1E1_Modelling.tex

+% !TEX program = xelatex
+
+\documentclass[aspectratio=169,20pt, USenglish]{beamer}
+
+\usetheme{ost}
+\input{include/beamer_basics.tex} % PACKAGES
+\input{include/mathsymbols.tex}   % SYMBOLS
+
+
+\graphicspath{{../static/img/}}
+
+\title{Introduction to recursive probabilistic learning}
+\subtitle{Modelling}
+\date{01.10.2021}
+\author{JuanPi Carbajal}
+\institute{Institute for Energy Technology}
+
+\begin{document}
+
+    \begin{frame}
+        \titlepage
+    \end{frame}
+
+    \section{Overview}
+    % Bayesian setup
+    % Prediction, filtering, smoothing
+    % Filters
+    % Smoothers
+    % Parameter estimation
+
+    \begin{frame}{Our goal}
+        \only<1>{
+            Illustrate the data and mention the process generating it
+        }
+        \note<1>{
+
+        }
+
+
+        \only<2>{
+            Show how we put our understanding of the process into a model
+        }
+        \note<2>{
+
+        }
+
+        \only<3>{
+            Show an animation of the working system
+        }
+        \note<3>{
+
+        }
+    \end{frame}
+
+    \begin{frame}{Representing our understading of the process}
+        Explain what types of models we can use we the tools we are going to learn
+    \end{frame}
+
+    \interludeframe{\textbf{\large Coffee break: 20 minutes}}
+
+    \section{Iterated maps}
+
+    \section{Ordinary differential equations}
+
+        \subsection{Discretization}
+
+    \interludeframe{\textbf{\large Lunch: 1 hour 30 minutes}}
+
+    \section{Errror propagation}
+
+    \section{Gaussian Distribution}
+    % Gaussian distribution in 1D
+    % Gaussian distribution in 2D
+    % Gaussian distribution in more dimensions
+    % Linear transformation in 1D: analytic and visual
+    % Nonlinear transformation in 1D: visual
+    % Linear and nonlinear transformation in 2D: visual
+
+    \interludeframe{\textbf{\large Coffee break: 30 minutes}}
+
+    \section{Stochastic modelling}
+    % X vs. Y plots: causal and probabilistic interpretation
+
+        \subsection{Statistical dependence and causality}
+
+    \section{Wrap-up}
+
+\end{document}