update title, chapter colour..

This commit is contained in:
Andreaierardi 2020-04-13 12:52:55 +02:00
parent cd82907763
commit b5aa522402
36 changed files with 4369 additions and 652 deletions

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\chapter{Lecture 1 - 09-03-2020}
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This is time for all good men to come to the aid of their party!
MACHINE LEARNING

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@ -1,9 +1,9 @@
\documentclass[../main.tex]{subfiles}
\begin{document}
\section{Lecture 2 - 07-04-2020}
\chapter{Lecture 2 - 07-04-2020}
\subsection{Argomento}
\section{Argomento}
Classification tasks\\
Semantic label space Y\\
Categorization Y finite and\\ small
@ -27,8 +27,8 @@ $
Losses for regression?\\
$y$, and $\hat{y} \in \barra{R}$, \\so they are numbers!\\
One example of loss is the absolute loss: absolute difference between numbers\\
\subsection{Loss}
\subsubsection{Absolute Loss}
\section{Loss}
\subsection{Absolute Loss}
$$\ell(y,\hat{y} = | y - \hat{y} | \Rightarrow absolute \quad loss\\ $$
--- DISEGNO ---\\\\
Some inconvenient properties:
@ -38,7 +38,7 @@ Some inconvenient properties:
\item Derivative only two values (not much informations)
\end{itemize}
\subsubsection{Square Loss}
\subsection{Square Loss}
$$ \ell(y,\hat{y} = ( y - \hat{y} )^2 \Rightarrow \textit{square loss}\\$$
-- DISEGNO ---\\
Derivative :
@ -50,7 +50,7 @@ Real numbers as label $\rightarrow$ regression.\\
Whenever taking difference between two prediction make sense (value are numbers) then we are talking about regression problem.\\
Classification as categorization when we have small finite set.\\\\
\subsubsection{Example of information of square loss}
\subsection{Example of information of square loss}
$\ell(y,\hat{y}) = ( y - \hat{y} )^2 = F(y)
\\
@ -106,7 +106,7 @@ $
The algorithm will be punish high more the prediction is not real. Algorithm will not get 0 and 1 because for example is impossible to get a perfect prediction.\\
This loss is useful to give this information to the algorithm.\\\\
Now we talk about labels and losses\\
\subsubsection{labels and losses}
\subsection{labels and losses}
Data points: they have some semantic labels that denote some true about this data points and we want to predict this labels.\\
We need to define what data points are: number? Strings? File? Typically they are stored in database records \\
They can have very precise structure or more homogeneously structured \\
@ -154,7 +154,7 @@ There is a way to encode a set of documents in point in a fixed dimensional
space in such way it make sense this coordinate are comparable.\\
I can represent fields with [0,1] for Neural network for example. But they have no geometrical meaning\\
\subsubsection{Example TF(idf) documents encoding}
\subsection{Example TF(idf) documents encoding}
TF encoding of docs.
\begin{enumerate}
\item Extract where all the words from docs

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@ -1,7 +1,7 @@
\documentclass[../main.tex]{subfiles}
\begin{document}
\section{Lecture 3 - 07-04-2020}
\chapter{Lecture 3 - 07-04-2020}
Data point x represented as sequences of measurement and we called this
measurements features or attributes.\\
$$ x = (x_1,..., x_d) \qquad x_1 \quad \textit{feature value}
@ -74,7 +74,7 @@ which we can avoid this to happen by design:
We want when we run ERM choosing a good predictor with ...... PD\\\\
\subsection{Overfitting}
\section{Overfitting}
We called this as overfitting: specific situation in which A (where A is the
learning algorithm) overfits if f output by A tends to have a training error much
smaller than the test error.\\
@ -84,7 +84,7 @@ Minimising training error doesnt mean minimising test error. Overfitting is b
Why this happens?\\
This happen because we have \textbf{noise in the data}\\
\subsubsection{Noise in the data}
\subsection{Noise in the data}
Noise in the data: $y_t$ is not deterministically associated with $x_i$.\\\\
Could be that datapoint appears more times in the same test set.
@ -160,7 +160,7 @@ define which predictor is good.\\
\subsection{Underfitting}
\section{Underfitting}
A underfits when f output by A has training error close to test error but they
are both large.\\
Close error test and training error is good but the are both large.
@ -183,7 +183,7 @@ $$
m >> ln |F| \qquad when \quad |F| < \infty \textit{\\ where m is the size of traning set}
$$
\\
\subsection{Nearest neighbour}
\section{Nearest neighbour}
This is completely different from ERM and is one of the first learning
algorithm. This exploit the geometry of the data.
Assume that our data space X is:

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@ -1,7 +1,7 @@
\documentclass[../main.tex]{subfiles}
\begin{document}
\section{Lecture 4 - 07-04-2020}
\chapter{Lecture 4 - 07-04-2020}
We spoke about Knn classifier with voronoi diagram
@ -11,7 +11,7 @@ $$
\\
$\hnn$ predictor needs to store entire dataset.
\\
\subsection{Computing $\hnn$}
\section{Computing $\hnn$}
Computing $\hnn(x)$ requires computing distances between x and points in the traning set.
\\
$$
@ -69,7 +69,7 @@ There are some heuristic to run NN algorithm without value of $k$.
\\
\end{itemize}
\subsection{Tree Predictor}
\section{Tree Predictor}
If a give you data not welled defined in a Euclidean space.
\\
$X = X_1 \cdot x \cdot ... \cdot X_d \cdot x$ \qquad Medical Record

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\usepackage{framed}
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%Options: Sonny, Lenny, Glenn, Conny, Rejne, Bjarne, Bjornstrup
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@ -28,14 +30,17 @@
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\babel@toc {english}{}
\contentsline {chapter}{\numberline {1}Lecture 1 - 09-03-2020}{2}%
\contentsline {section}{\numberline {1.1}Introduction}{2}%
\contentsline {chapter}{\numberline {2}Lecture 2 - 07-04-2020}{5}%
\contentsline {section}{\numberline {2.1}Argomento}{5}%
\contentsline {section}{\numberline {2.2}Loss}{5}%
\contentsline {subsection}{\numberline {2.2.1}Absolute Loss}{5}%
\contentsline {subsection}{\numberline {2.2.2}Square Loss}{6}%
\contentsline {subsection}{\numberline {2.2.3}Example of information of square loss}{6}%
\contentsline {subsection}{\numberline {2.2.4}labels and losses}{7}%
\contentsline {subsection}{\numberline {2.2.5}Example TF(idf) documents encoding}{9}%
\contentsline {chapter}{\numberline {3}Lecture 3 - 07-04-2020}{11}%
\contentsline {section}{\numberline {3.1}Overfitting}{13}%
\contentsline {subsection}{\numberline {3.1.1}Noise in the data}{13}%
\contentsline {section}{\numberline {3.2}Underfitting}{14}%
\contentsline {section}{\numberline {3.3}Nearest neighbour}{15}%
\contentsline {chapter}{\numberline {4}Lecture 4 - 07-04-2020}{17}%
\contentsline {section}{\numberline {4.1}Computing $h_{NN}$}{17}%
\contentsline {section}{\numberline {4.2}Tree Predictor}{18}%
\contentsline {chapter}{\numberline {5}Lecture 5 - 07-04-2020}{21}%
\contentsline {section}{\numberline {5.1}Tree Classifier}{21}%
\contentsline {section}{\numberline {5.2}Jensens inequality}{22}%
\contentsline {section}{\numberline {5.3}Tree Predictor}{24}%
\contentsline {section}{\numberline {5.4}Statistical model for Machine Learning}{25}%
\contentsline {chapter}{\numberline {6}Lecture 6 - 07-04-2020}{27}%
\contentsline {chapter}{\numberline {7}Lecture 7 - 07-04-2020}{28}%
\contentsline {chapter}{\numberline {8}Lecture 8 - 07-04-2020}{29}%
\contentsline {chapter}{\numberline {9}Lecture 9 - 07-04-2020}{30}%
\contentsline {chapter}{\numberline {10}Lecture 10 - 07-04-2020}{31}%
\contentsline {section}{\numberline {10.1}TO BE DEFINE}{31}%