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Andrea Ierardi \\ - Data Science and Economcis\\ - Università degli Studi di Milano\\ - -} -\date{\today} - \documentclass[12pt]{article} \usepackage{amsmath} \usepackage{systeme} @@ -14,6 +5,9 @@ \usepackage{subfiles} \usepackage[english]{babel} \usepackage[dvipsnames]{xcolor} +\usepackage{graphicx} + +\graphicspath{ {./img/} } \definecolor{mypink}{cmyk}{0, 0.7808, 0.4429, 0.1412} \definecolor{mygray}{gray}{0.6} \usepackage{framed} @@ -27,8 +21,40 @@ \newcommand\red[1]{\textcolor{BrickRed}{#1}} \newcommand\gray[1]{\textcolor{mygray}{#1}} + \begin{document} -\maketitle + +\begin{titlepage} + \begin{center} + \vspace*{1cm} + + \Huge + \textbf{Statistical Methods for Machine Learning} + + \vspace{0.5cm} + \LARGE + Data Science and Economics + + \vspace{1.5cm} + + \textbf{Andrea Ierardi} + + \vfill + + Lecture notes + + \vspace{0.8cm} + + \includegraphics[width=0.4\textwidth]{frontpage} + + \Large + Università degli Studi di Milano + \\ + Milan\\ + 12/04/2020 + + \end{center} +\end{titlepage} \newpage diff --git a/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/main.toc b/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/main.toc index 1805d271a..729702441 100644 --- a/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/main.toc +++ b/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/main.toc @@ -1,30 +1,30 @@ \babel@toc {english}{} -\contentsline {section}{\numberline {1}Lecture 1 - 09-03-2020}{4}% -\contentsline {subsection}{\numberline {1.1}Introduction}{4}% -\contentsline {section}{\numberline {2}Lecture 2 - 07-04-2020}{7}% -\contentsline {subsection}{\numberline {2.1}Argomento}{7}% -\contentsline {subsection}{\numberline {2.2}Loss}{7}% -\contentsline {subsubsection}{\numberline {2.2.1}Absolute Loss}{7}% -\contentsline {subsubsection}{\numberline {2.2.2}Square Loss}{8}% -\contentsline {subsubsection}{\numberline {2.2.3}Example of information of square loss}{8}% -\contentsline {subsubsection}{\numberline {2.2.4}labels and losses}{10}% -\contentsline {subsubsection}{\numberline {2.2.5}Example TF(idf) documents encoding}{11}% -\contentsline {section}{\numberline {3}Lecture 3 - 07-04-2020}{13}% -\contentsline {subsection}{\numberline {3.1}Overfitting}{15}% -\contentsline {subsubsection}{\numberline {3.1.1}Noise in the data}{15}% -\contentsline {subsection}{\numberline {3.2}Underfitting}{17}% -\contentsline {subsection}{\numberline {3.3}Nearest neighbour}{17}% -\contentsline {section}{\numberline {4}Lecture 4 - 07-04-2020}{19}% -\contentsline {subsection}{\numberline {4.1}Computing $h_{NN}$}{19}% -\contentsline {subsection}{\numberline {4.2}Tree Predictor}{20}% -\contentsline {section}{\numberline {5}Lecture 5 - 07-04-2020}{23}% -\contentsline {subsection}{\numberline {5.1}Tree Classifier}{23}% -\contentsline {subsection}{\numberline {5.2}Jensen’s inequality}{24}% -\contentsline {subsection}{\numberline {5.3}Tree Predictor}{26}% -\contentsline {subsection}{\numberline {5.4}Statistical model for Machine Learning}{27}% -\contentsline {section}{\numberline {6}Lecture 6 - 07-04-2020}{29}% -\contentsline {section}{\numberline {7}Lecture 7 - 07-04-2020}{30}% -\contentsline {section}{\numberline {8}Lecture 8 - 07-04-2020}{31}% -\contentsline {section}{\numberline {9}Lecture 9 - 07-04-2020}{32}% -\contentsline {section}{\numberline {10}Lecture 10 - 07-04-2020}{33}% -\contentsline {subsection}{\numberline {10.1}TO BE DEFINE}{33}% +\contentsline {section}{\numberline {1}Lecture 1 - 09-03-2020}{3}% +\contentsline {subsection}{\numberline {1.1}Introduction}{3}% +\contentsline {section}{\numberline {2}Lecture 2 - 07-04-2020}{6}% +\contentsline {subsection}{\numberline {2.1}Argomento}{6}% +\contentsline {subsection}{\numberline {2.2}Loss}{6}% +\contentsline {subsubsection}{\numberline {2.2.1}Absolute Loss}{6}% +\contentsline {subsubsection}{\numberline {2.2.2}Square Loss}{7}% +\contentsline {subsubsection}{\numberline {2.2.3}Example of information of square loss}{7}% +\contentsline {subsubsection}{\numberline {2.2.4}labels and losses}{9}% +\contentsline {subsubsection}{\numberline {2.2.5}Example TF(idf) documents encoding}{10}% +\contentsline {section}{\numberline {3}Lecture 3 - 07-04-2020}{12}% +\contentsline {subsection}{\numberline {3.1}Overfitting}{14}% +\contentsline {subsubsection}{\numberline {3.1.1}Noise in the data}{14}% +\contentsline {subsection}{\numberline {3.2}Underfitting}{16}% +\contentsline {subsection}{\numberline {3.3}Nearest neighbour}{16}% +\contentsline {section}{\numberline {4}Lecture 4 - 07-04-2020}{18}% +\contentsline {subsection}{\numberline {4.1}Computing $h_{NN}$}{18}% +\contentsline {subsection}{\numberline {4.2}Tree Predictor}{19}% +\contentsline {section}{\numberline {5}Lecture 5 - 07-04-2020}{22}% +\contentsline {subsection}{\numberline {5.1}Tree Classifier}{22}% +\contentsline {subsection}{\numberline {5.2}Jensen’s inequality}{23}% +\contentsline {subsection}{\numberline {5.3}Tree Predictor}{25}% +\contentsline {subsection}{\numberline {5.4}Statistical model for Machine Learning}{26}% +\contentsline {section}{\numberline {6}Lecture 6 - 07-04-2020}{28}% +\contentsline {section}{\numberline {7}Lecture 7 - 07-04-2020}{29}% +\contentsline {section}{\numberline {8}Lecture 8 - 07-04-2020}{30}% +\contentsline {section}{\numberline {9}Lecture 9 - 07-04-2020}{31}% +\contentsline {section}{\numberline {10}Lecture 10 - 07-04-2020}{32}% +\contentsline {subsection}{\numberline {10.1}TO BE DEFINE}{32}%