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See the LaTeX manual or LaTeX Companion for explanation.
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\OML/cmm/m/it/12 A \OMS/cmsy/m/n/12 \OML/cmm/m/it/12 ERM[] \OMS/cmsy/m/n/12 !
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LaTeX Warning: Command \textquoteright invalid in math mode on input line 163.
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[22] (lectures/lecture5.tex
|
||||
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|
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[27]) [28] (lectures/lecture6.tex) [29] (lectures/lecture7.tex) [30]
|
||||
(lectures/lecture8.tex) [31] (lectures/lecture9.tex) [32]
|
||||
[26]) [27] (lectures/lecture6.tex) [28] (lectures/lecture7.tex) [29]
|
||||
(lectures/lecture8.tex) [30] (lectures/lecture9.tex) [31]
|
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(lectures/lecture10.tex
|
||||
|
||||
LaTeX Font Warning: Command \small invalid in math mode on input line 9.
|
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[]
|
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|
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[34]) [35] (main.bbl
|
||||
[33]) [34] (main.bbl
|
||||
|
||||
LaTeX Warning: Empty `thebibliography' environment on input line 3.
|
||||
|
||||
) [36] (main.aux) )
|
||||
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|
||||
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|
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|
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30i,11n,37p,321b,258s stack positions out of 5000i,500n,10000p,200000b,50000s
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<C:\Users\AndreDany\AppData\Local\MiKTeX\2.9\fonts/pk/ljfou
|
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r/jknappen/ec/dpi600\tcti1200.pk> <C:\Users\AndreDany\AppData\Local\MiKTeX\2.9\
|
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fonts/pk/ljfour/jknappen/ec/dpi600\tcrm1200.pk><E:/Program Files/MiKTeX 2.9/fon
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ts/type1/public/amsfonts/cm/cmbx12.pfb><E:/Program Files/MiKTeX 2.9/fonts/type1
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amsfonts/cm/cmmi10.pfb><E:/Program Files/MiKTeX 2.9/fonts/type1/public/amsfonts
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6.pfb><E:/Program Files/MiKTeX 2.9/fonts/type1/public/amsfonts/cm/cmmi8.pfb><E:
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Files/MiKTeX 2.9/fonts/type1/public/amsfonts/cm/cmr17.pfb><E:/Program Files/MiK
|
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TeX 2.9/fonts/type1/public/amsfonts/cm/cmr6.pfb><E:/Program Files/MiKTeX 2.9/fo
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nts/type1/public/amsfonts/cm/cmr8.pfb><E:/Program Files/MiKTeX 2.9/fonts/type1/
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public/amsfonts/cm/cmsy10.pfb><E:/Program Files/MiKTeX 2.9/fonts/type1/public/a
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msfonts/cm/cmsy6.pfb><E:/Program Files/MiKTeX 2.9/fonts/type1/public/amsfonts/c
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\title{Statistical Methods for Machine Learning}
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\author{
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Andrea Ierardi \\
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Data Science and Economcis\\
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Università degli Studi di Milano\\
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}
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\date{\today}
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\documentclass[12pt]{article}
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\usepackage{amsmath}
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\usepackage{systeme}
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\usepackage{subfiles}
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\usepackage[english]{babel}
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\usepackage[dvipsnames]{xcolor}
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\usepackage{graphicx}
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\graphicspath{ {./img/} }
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\definecolor{mypink}{cmyk}{0, 0.7808, 0.4429, 0.1412}
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\definecolor{mygray}{gray}{0.6}
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\usepackage{framed}
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@ -27,8 +21,40 @@
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\newcommand\red[1]{\textcolor{BrickRed}{#1}}
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\newcommand\gray[1]{\textcolor{mygray}{#1}}
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\begin{document}
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\maketitle
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\begin{titlepage}
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\begin{center}
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\vspace*{1cm}
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\Huge
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\textbf{Statistical Methods for Machine Learning}
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\vspace{0.5cm}
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\LARGE
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Data Science and Economics
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\vspace{1.5cm}
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\textbf{Andrea Ierardi}
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\vfill
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Lecture notes
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\vspace{0.8cm}
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\includegraphics[width=0.4\textwidth]{frontpage}
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\Large
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Università degli Studi di Milano
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\\
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Milan\\
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12/04/2020
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\end{center}
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\end{titlepage}
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\newpage
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@ -1,30 +1,30 @@
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\babel@toc {english}{}
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\contentsline {section}{\numberline {1}Lecture 1 - 09-03-2020}{4}%
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\contentsline {subsection}{\numberline {1.1}Introduction}{4}%
|
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\contentsline {section}{\numberline {2}Lecture 2 - 07-04-2020}{7}%
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\contentsline {subsection}{\numberline {2.1}Argomento}{7}%
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\contentsline {subsection}{\numberline {2.2}Loss}{7}%
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\contentsline {subsubsection}{\numberline {2.2.1}Absolute Loss}{7}%
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\contentsline {subsubsection}{\numberline {2.2.2}Square Loss}{8}%
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\contentsline {subsubsection}{\numberline {2.2.3}Example of information of square loss}{8}%
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\contentsline {subsubsection}{\numberline {2.2.4}labels and losses}{10}%
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\contentsline {subsubsection}{\numberline {2.2.5}Example TF(idf) documents encoding}{11}%
|
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\contentsline {section}{\numberline {3}Lecture 3 - 07-04-2020}{13}%
|
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\contentsline {subsection}{\numberline {3.1}Overfitting}{15}%
|
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\contentsline {subsubsection}{\numberline {3.1.1}Noise in the data}{15}%
|
||||
\contentsline {subsection}{\numberline {3.2}Underfitting}{17}%
|
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\contentsline {subsection}{\numberline {3.3}Nearest neighbour}{17}%
|
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\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}%
|
||||
|
||||
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