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3636 strings out of 480934 - 50556 string characters out of 2909674 - 313110 words of memory out of 3000000 - 19420 multiletter control sequences out of 15000+200000 + 3644 strings out of 480934 + 50616 string characters out of 2909674 + 314110 words of memory out of 3000000 + 19425 multiletter control sequences out of 15000+200000 544831 words of font info for 69 fonts, out of 3000000 for 9000 1141 hyphenation exceptions out of 8191 - 30i,11n,37p,1147b,259s stack positions out of 5000i,500n,10000p,200000b,50000s + 30i,11n,37p,321b,257s stack positions out of 5000i,500n,10000p,200000b,50000s -Output written on main.dvi (36 pages, 66348 bytes). +Output written on main.dvi (36 pages, 72912 bytes). diff --git a/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/main.tex b/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/main.tex index d5d52ccfc..5dcc973c8 100644 --- a/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/main.tex +++ b/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/main.tex @@ -45,7 +45,7 @@ \vspace{0.8cm} - \includegraphics[width=.7\linewidth]{frontpage.jpg} + \includegraphics[width=.7\linewidth]{frontpage} \Large Università degli Studi di Milano 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 000546b0f..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}{2}% -\contentsline {subsection}{\numberline {1.1}Introduction}{2}% -\contentsline {section}{\numberline {2}Lecture 2 - 07-04-2020}{5}% -\contentsline {subsection}{\numberline {2.1}Argomento}{5}% -\contentsline {subsection}{\numberline {2.2}Loss}{5}% -\contentsline {subsubsection}{\numberline {2.2.1}Absolute Loss}{5}% -\contentsline {subsubsection}{\numberline {2.2.2}Square Loss}{6}% -\contentsline {subsubsection}{\numberline {2.2.3}Example of information of square loss}{6}% -\contentsline {subsubsection}{\numberline {2.2.4}labels and losses}{8}% -\contentsline {subsubsection}{\numberline {2.2.5}Example TF(idf) documents encoding}{9}% -\contentsline {section}{\numberline {3}Lecture 3 - 07-04-2020}{11}% -\contentsline {subsection}{\numberline {3.1}Overfitting}{13}% -\contentsline {subsubsection}{\numberline {3.1.1}Noise in the data}{13}% -\contentsline {subsection}{\numberline {3.2}Underfitting}{15}% -\contentsline {subsection}{\numberline {3.3}Nearest neighbour}{15}% -\contentsline {section}{\numberline {4}Lecture 4 - 07-04-2020}{17}% -\contentsline {subsection}{\numberline {4.1}Computing $h_{NN}$}{17}% -\contentsline {subsection}{\numberline {4.2}Tree Predictor}{18}% -\contentsline {section}{\numberline {5}Lecture 5 - 07-04-2020}{21}% -\contentsline {subsection}{\numberline {5.1}Tree Classifier}{21}% -\contentsline {subsection}{\numberline {5.2}Jensen’s inequality}{22}% -\contentsline {subsection}{\numberline {5.3}Tree Predictor}{24}% -\contentsline {subsection}{\numberline {5.4}Statistical model for Machine Learning}{25}% -\contentsline {section}{\numberline {6}Lecture 6 - 07-04-2020}{27}% -\contentsline {section}{\numberline {7}Lecture 7 - 07-04-2020}{28}% -\contentsline {section}{\numberline {8}Lecture 8 - 07-04-2020}{29}% -\contentsline {section}{\numberline {9}Lecture 9 - 07-04-2020}{30}% -\contentsline {section}{\numberline {10}Lecture 10 - 07-04-2020}{31}% -\contentsline {subsection}{\numberline {10.1}TO BE DEFINE}{31}% +\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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