MachineLearningNotes/main.tex at main · federicobrancasi/MachineLearningNotes · GitHub

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\documentclass[oneside]{book}

\input{prefix}

\title{\Huge \textbf{Machine Learning\\} \Large{Andrea Passerini\\a.y. 2022/2023}}

\author{Stefano Genetti\\Vittoria Ossanna\\Giovanni Valer (\LaTeX\ revision)}

\begin{document}
\maketitle

\newpage
\textit{These notes are for the course "Machine Learning" held by professor Andrea Passerini. These notes do not directly cover the laboratory part; still, while discussing the theory many concepts applicable to the laboratory part might be covered. \\
We tried to include most of the materials used during the lectures or given as reference. Our aim was to create notes that allow to get a clear overview of all the topics and to arrange them in a structured manner, while also being fairly exhaustive.\\
We tried our best to avoid mistakes while compiling these notes. That being said, a very broad range of topics was to be covered, hence we do not take responsibility for any mistake or imperfection that might be present; still, it would be much appreciated if you reported any of those to us so that we can fix the notes accordingly.\\
In this \href{https://github.com/StefanoGenettiUniTN/MachineLearning_Passerini.git}{Github repository} you can find the \LaTeX source code, you are free to clone the project and modify it by yourself or just create an issue to include topics and corrections.\\
We hope that this document will help in the study of this beautiful and exciting subject.}
\newline

\textit{If you consider this material valuable for you in order to be prepared for the exam, consider offering us a coffee (-:}\\
Paypal: vittossanna@gmail.com
\newline

\textit{XOXO - the authors}
\newpage

\tableofcontents

\input{chapters/01_Introduction}
\input{chapters/02_DecisionTrees}
\input{chapters/03_KNearestNeighbours}
\input{chapters/04_LinearAlgebra}
\input{chapters/05_ProbabilityTheory}
\input{chapters/06_Evaluation}
\input{chapters/07_ParameterEstimation}
\input{chapters/08_BayesianNetworks}
\input{chapters/09_BayesianNetworksInference}
\input{chapters/10_BayesianNetworksLearning}
\input{chapters/11_NaiveBayes}
\input{chapters/12_LinearDiscriminantFunctions}
\input{chapters/13_SupportVectorMachines}
\input{chapters/14_NonLinearSVMs}
\input{chapters/15_KernelMachines}
\input{chapters/16_DeepLearning}
\input{chapters/17_EnsembleMethods}
\input{chapters/18_UnsupervisedLearning}
\input{chapters/19_ReinforcementLearning}

\end{document}