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PDF statistics: - 265 PDF objects out of 1000 (max. 8388607) + 270 PDF objects out of 1000 (max. 8388607) 0 named destinations out of 1000 (max. 500000) 1 words of extra memory for PDF output out of 10000 (max. 10000000) diff --git a/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/lectures/lecture6.pdf b/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/lectures/lecture6.pdf index 15ccdb496..c445ddaf3 100644 Binary files a/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/lectures/lecture6.pdf and b/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/lectures/lecture6.pdf differ diff --git a/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/lectures/lecture6.synctex.gz b/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/lectures/lecture6.synctex.gz index fed710cc1..3f43a8626 100644 Binary files a/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/lectures/lecture6.synctex.gz and b/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/lectures/lecture6.synctex.gz differ diff --git a/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/lectures/lecture6.tex b/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/lectures/lecture6.tex index 2ec919ce1..5fcd1263e 100644 --- a/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/lectures/lecture6.tex +++ b/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/lectures/lecture6.tex @@ -217,7 +217,12 @@ $$\ $$ \barra{E} \left[ \, \ell , f^*(x) \, \right] = \barra{E} \left[ \, min \, \{ \eta(x) , 1- \eta(x) \} \, \right] $$ -\includegraphics{bayesrisk.jpg} +\begin{figure}[h] + \centering + \includegraphics[width=1\textwidth]{bayesrisk.jpg} + \caption{Example of Bayes Risk} + %\label{fig:} +\end{figure} \\ Conditional risk will be high aroun the half so min between the two is around the half since the labels are random i will get an error near $50\%$.\\ diff --git a/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/main.aux b/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/main.aux index 7b7c46461..d11e29e4e 100644 --- a/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/main.aux +++ b/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/main.aux @@ -1,58 +1,59 @@ \relax \@nameuse{bbl@beforestart} \babel@aux{english}{} -\@writefile{toc}{\contentsline {chapter}{\numberline {1}Lecture 1 - 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\tableofcontents +%\listoftables +\listoffigures + \newpage \subfile{lectures/lecture1} 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 f447fbf42..b0e342c88 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 @@ -1,34 +1,34 @@ \babel@toc {english}{} -\contentsline {chapter}{\numberline {1}Lecture 1 - 09-03-2020}{3}% -\contentsline {section}{\numberline {1.1}Introduction}{3}% -\contentsline {chapter}{\numberline {2}Lecture 2 - 07-04-2020}{6}% -\contentsline {section}{\numberline {2.1}Argomento}{6}% -\contentsline {section}{\numberline {2.2}Loss}{6}% -\contentsline {subsection}{\numberline {2.2.1}Absolute Loss}{6}% -\contentsline {subsection}{\numberline {2.2.2}Square Loss}{7}% -\contentsline {subsection}{\numberline {2.2.3}Example of information of square loss}{7}% -\contentsline {subsection}{\numberline {2.2.4}labels and losses}{8}% -\contentsline {subsection}{\numberline {2.2.5}Example TF(idf) documents encoding}{10}% -\contentsline {chapter}{\numberline {3}Lecture 3 - 07-04-2020}{12}% -\contentsline {section}{\numberline {3.1}Overfitting}{14}% -\contentsline {subsection}{\numberline {3.1.1}Noise in the data}{14}% -\contentsline {section}{\numberline {3.2}Underfitting}{15}% -\contentsline {section}{\numberline {3.3}Nearest neighbour}{16}% -\contentsline {chapter}{\numberline {4}Lecture 4 - 07-04-2020}{18}% -\contentsline {section}{\numberline {4.1}Computing $h_{NN}$}{18}% -\contentsline {section}{\numberline {4.2}Tree Predictor}{19}% -\contentsline {chapter}{\numberline {5}Lecture 5 - 07-04-2020}{22}% -\contentsline {section}{\numberline {5.1}Tree Classifier}{22}% -\contentsline {section}{\numberline {5.2}Jensen’s inequality}{23}% -\contentsline {section}{\numberline {5.3}Tree Predictor}{25}% -\contentsline {section}{\numberline {5.4}Statistical model for Machine Learning}{26}% -\contentsline {chapter}{\numberline {6}Lecture 6 - 07-04-2020}{28}% -\contentsline {section}{\numberline {6.1}Bayes Optimal Predictor}{28}% -\contentsline {subsection}{\numberline {6.1.1}Square Loss}{29}% -\contentsline {subsection}{\numberline {6.1.2}Zero-one loss for binary classification}{30}% -\contentsline {section}{\numberline {6.2}Bayes Risk}{32}% -\contentsline {chapter}{\numberline {7}Lecture 7 - 07-04-2020}{33}% -\contentsline {chapter}{\numberline {8}Lecture 8 - 07-04-2020}{34}% -\contentsline {chapter}{\numberline {9}Lecture 9 - 07-04-2020}{35}% -\contentsline {chapter}{\numberline {10}Lecture 10 - 07-04-2020}{36}% -\contentsline {section}{\numberline {10.1}TO BE DEFINE}{36}% +\contentsline {chapter}{\numberline {1}Lecture 1 - 09-03-2020}{4}% +\contentsline {section}{\numberline {1.1}Introduction}{4}% +\contentsline {chapter}{\numberline {2}Lecture 2 - 07-04-2020}{7}% +\contentsline {section}{\numberline {2.1}Argomento}{7}% +\contentsline {section}{\numberline {2.2}Loss}{7}% +\contentsline {subsection}{\numberline {2.2.1}Absolute Loss}{7}% +\contentsline {subsection}{\numberline {2.2.2}Square Loss}{8}% +\contentsline {subsection}{\numberline {2.2.3}Example of information of square loss}{8}% +\contentsline {subsection}{\numberline {2.2.4}labels and losses}{9}% +\contentsline {subsection}{\numberline {2.2.5}Example TF(idf) documents encoding}{11}% +\contentsline {chapter}{\numberline {3}Lecture 3 - 07-04-2020}{13}% +\contentsline {section}{\numberline {3.1}Overfitting}{15}% +\contentsline {subsection}{\numberline {3.1.1}Noise in the data}{15}% +\contentsline {section}{\numberline {3.2}Underfitting}{16}% +\contentsline {section}{\numberline {3.3}Nearest neighbour}{17}% +\contentsline {chapter}{\numberline {4}Lecture 4 - 07-04-2020}{19}% +\contentsline {section}{\numberline {4.1}Computing $h_{NN}$}{19}% +\contentsline {section}{\numberline {4.2}Tree Predictor}{20}% +\contentsline {chapter}{\numberline {5}Lecture 5 - 07-04-2020}{23}% +\contentsline {section}{\numberline {5.1}Tree Classifier}{23}% +\contentsline {section}{\numberline {5.2}Jensen’s inequality}{24}% +\contentsline {section}{\numberline {5.3}Tree Predictor}{26}% +\contentsline {section}{\numberline {5.4}Statistical model for Machine Learning}{27}% +\contentsline {chapter}{\numberline {6}Lecture 6 - 07-04-2020}{29}% +\contentsline {section}{\numberline {6.1}Bayes Optimal Predictor}{29}% +\contentsline {subsection}{\numberline {6.1.1}Square Loss}{30}% +\contentsline {subsection}{\numberline {6.1.2}Zero-one loss for binary classification}{31}% +\contentsline {section}{\numberline {6.2}Bayes Risk}{33}% +\contentsline {chapter}{\numberline {7}Lecture 7 - 07-04-2020}{34}% +\contentsline {chapter}{\numberline {8}Lecture 8 - 07-04-2020}{35}% +\contentsline {chapter}{\numberline {9}Lecture 9 - 07-04-2020}{36}% +\contentsline {chapter}{\numberline {10}Lecture 10 - 07-04-2020}{37}% +\contentsline {section}{\numberline {10.1}TO BE DEFINE}{37}%