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first lessons
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Chapter 1.
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\begin{document}
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\chapter{Lecture 1 - 09-03-2020}
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\section{Introduction}
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This is time for all good men to come to the aid of their party!
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\section{Introduction of the course}
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MACHINE LEARNING
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In this course we look at the principle behind design of Machine learning.
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Not just coding but have an idea of algorithm that can work with the data.
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We have to fix a mathematical framework: some statistic and mathematics.
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Work on ML on a higher level
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Not just coding but have an idea of algorithm that can work with the data.\\\\
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We have to fix a mathematical framework: some statistic and mathematics.\\
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\textbf{Work on ML on a higher level}\\
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ML is data inference: make prediction about the future using data about the
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past
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Clustering —> grouping according to similarity
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Planning —> (robot to learn to interact in a certain environment)
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Classification —> (assign meaning to data) example: Spam filtering
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past\\
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\begin{itemize}
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\item Clustering $\rightarrow$ grouping according to similarity
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\item Planning $\rightarrow$ (robot to learn to interact in a certain environment)
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\item Classification $\rightarrow$ (assign meaning to data) example: Spam filtering\\
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I want to predict the outcome of this individual or i want to predict whether a
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person click or not in a certain advertisement.
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Examples
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Classify data into categories:
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Medical diagnosis: data are medical records and • categories are diseases
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• Document analysis: data are texts and categories are topics
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• Image analysts: data are digital images and for categories name of objects
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\end{itemize}
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\section{Examples}
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Classify data into categories:\\
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\begin{itemize}
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\item Medical diagnosis: data are medical records and categories are diseases
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\item Document analysis: data are texts and categories are topics
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\item Image analysts: data are digital images and for categories name of objects
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in the image (but could be different).
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• Spam filtering: data are emails, categories are spam vs non spam.
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• Advertising prediction: data are features of web site visitors and categories
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\item Spam filtering: data are emails, categories are spam vs non spam.
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\item Advertising prediction: data are features of web site visitors and categories
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could be click/non click on banners.
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Classification : Different from clustering since we do not have semantically
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classification (spam or not spam) —> like meaning of the image.
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\end{itemize}
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Classification : \textbf{ Different from clustering }since we do not have semantically
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classification (spam or not spam) $\rightarrow$ like meaning of the image.\\
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I have a semantic label.
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Clustering: i want to group data with similarity function.
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Planning: Learning what to do next
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Clustering: Learn similarity function
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Classification: Learn semantic labels meaning of data
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Planning: Learn actions given state
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\\\\
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Clustering: i want to group data with similarity function. \\\\
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Planning: Learning what to do next \\\\
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Clustering: Learn similarity function \\\\
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Classification: Learn semantic labels meaning of data\\\\
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Planning: Learn actions given state\\\\
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In classification is an easier than planning task since I’m able to make
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prediction telling what is the semantic label that goes with data points.
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If i can do classification i can clustering.
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prediction telling what is the semantic label that goes with data points.\\
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If i can do classification i can clustering.\\
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If you do planning you probably classify (since you understanding meaning in
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your position) and then you can also do clustering probably.
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We will focus on classification because many tasks are about classification.
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Classify data in categories we can image a set of categories.
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For instance the tasks:
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‘predict income of a person’
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‘Predict tomorrow price for a stock’
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The label is a number and not an abstract thing.
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your position) and then you can also do clustering probably.\\
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We will focus on classification because many tasks are about classification.\\\\
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Classify data in categories we can image a set of categories.\\
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For instance the tasks:\\
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‘predict income of a person’\\
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‘Predict tomorrow price for a stock’\\
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The label is a number and not an abstract thing.\\\\
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We can distinguish two cases:
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The label set —> set of possible categories for each data • point. For each of
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\begin{itemize}
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\item The label set $\rightarrow$ set of possible categories for each data point. For each of
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this could be finite set of abstract symbols (case of document classification,
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medical diagnosis). So the task is classification.
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• Real number (no bound on how many of them). My prediction will be a real
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\item Real number (no bound on how many of them). My prediction will be a real
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number and is not a category. In this case we talk about a task of
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regression.
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\end{itemize}
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Classification: task we want to give a label predefined point in abstract
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categories (like YES or NO)
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\\
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Regression: task we want to give label to data points but this label are
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numbers.
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numbers.\\\\
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When we say prediction task: used both for classification and regression
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tasks.
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Supervised learning: Label attached to data (classification, regression)
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Unsupervised learning: No labels attached to data (clustering)
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tasks.\\
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Supervised learning: Label attached to data (classification, regression)\\
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Unsupervised learning: No labels attached to data (clustering)\\
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In unsupervised the mathematical modelling and way algorithm are score and
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can learn from mistakes is a little bit harder. Problem of clustering is harder to
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model mathematically.
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model mathematically.\\
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You can cast planning as supervised learning: i can show the robot which is
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the right action to do in that state. But that depends on planning task is
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formalised.
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formalised.\\
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Planning is higher level of learning since include task of supervised and
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unsupervised learning.
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Why is this important ?
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Algorithm has to know how to given the label.
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unsupervised learning.\\\\
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Why is this important ?\\
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Algorithm has to know how to given the label.\\
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In ML we want to teach the algorithm to perform prediction correctly. Initially
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algorithm will make mistakes in classifying data. We want to tell algorithm that
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classification was wrong and just want to perform a score. Like giving a grade
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to the algorithm to understand if it did bad or really bad.
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So we have mistakes!
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Algorithm predicts and something makes a mistake —> we can correct it.
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So we have mistakes!\\\\
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Algorithm predicts and something makes a mistake $\rightarrow$ we can correct it.\\
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Then algorithm can be more precisely.
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We have to define this mistake.
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Mistakes in case of classification:
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If category is the wrong one (in the simple case). We • have a binary signal
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We have to define this mistake.\\
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Mistakes in case of classification:\\
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\begin{itemize}
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\item If category is the wrong one (in the simple case). We have a binary signal
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where we know that category is wrong.
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How to communicate it?
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\end{itemize}
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How to communicate it?\\
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We can use the loss function: we can tell the algorithm whether is wrong or
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not.
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Loss function: measure discrepancy between ‘true’ label and predicted
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label.
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not.\\\\
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\bred{Loss function}: measure discrepancy between ‘true’ label and predicted
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label.\\
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So we may assume that every datapoint has a true label.
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If we have a set of topic this is the true topic that document is talking about.
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It is typical in supervised learning.
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were $y $ is true label and $\hat{y}$ is predicted label
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\\\\
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We want to build a spam filter were $0$ is not spam and $1$ is spam and that
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We want to build a spam filter were $0$ is not spam and $1$ is spam and that's a
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Classification task:
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\\\\
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$
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\end{cases}
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$
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\\\\
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The loss function is the “interface” between algorithm and data.
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So algorithm know about the data through the loss function.
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\textbf{The loss function is the “interface” between algorithm and data.}\\
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So algorithm know about the data through the loss function.\\
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If we give a useless loss function the algorithm will not perform good: is
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important to have a good loss function.
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Spam filtering
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We have two main mistakes:
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\subsection{Spam filtering}
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$Y = \{ spam, no \, spam\}$
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\\
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Binary classification $|Y| = 2$ \\
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We have two main mistake:
|
||||
\begin{itemize}
|
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\item False positive: $y = $non spam, $\hat{y} = $ spam
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\item False negative: $y = $spam, $\hat{y} = $ no spam
|
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\end{itemize}
|
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It is the same mistake? No if i have important email and you classify as spam
|
||||
that’s bad and if you show me a spam than it’s ok.
|
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So we have to assign a different weight.
|
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that’s bad and if you show me a spam than it’s ok.\\
|
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So we have to assign a different weight.\\
|
||||
$$
|
||||
\ell\left(y,\hat{y}\right) = \begin{cases}
|
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2 \quad \textit{if FP}\\
|
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1 \quad \textit{if FN}\\
|
||||
0 \quad otherwise
|
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\end{cases}
|
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$$
|
||||
\bred{We have to take more attention on positive mistake}\\
|
||||
Even in binary classification, mistakes are not equal.
|
||||
e Iotf.TFprIuos.uos
|
||||
True came
|
||||
razee
|
||||
Cussler aircN TASK spam ACG FIRM
|
||||
ftp.y GO
|
||||
IF F Y n is soon
|
||||
IF FEY 0 Nor spam
|
||||
ZERO CNE Cass
|
||||
n n
|
||||
Span No Seamy Binary Classification
|
||||
I 2
|
||||
FALSE PEENE Mistake Y NON SPAM J Spam
|
||||
FN Mistake i f SPAM y NO spam
|
||||
2 IF Fp Meter Airenita
|
||||
f Y F on positive
|
||||
y ye en MISTAKE
|
||||
0 otherwise
|
||||
0 otherwise
|
||||
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\end{document}
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||||
@ -6,8 +6,8 @@
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This is pdfTeX, Version 3.14159265-2.6-1.40.21 (MiKTeX 2.9.7300 64-bit) (preloaded format=pdflatex 2020.4.13) 15 APR 2020 21:01
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entering extended mode
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Reference in New Issue
Block a user