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adding some other datasets to project 2

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mhjensen
2019-10-17 05:05:00 +02:00
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7 changed files with 62 additions and 4 deletions
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doc/Projects/2019/Project2/html/Project2-bs.html
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@@ -153,7 +153,7 @@ MathJax.Hub.Config({
<center><b>Department of Physics, University of Oslo, Norway</b></center>
<br>
<p>
<center><h4>Oct 9, 2019</h4></center> <!-- date -->
<center><h4>Oct 17, 2019</h4></center> <!-- date -->
<br>
<p>
</div> <!-- end jumbotron -->
@@ -195,6 +195,9 @@ has been studied in the scientific literature. This makes it easier
for you to compare and analyze your results. It is also an essential
elements of the scientific discussion.
<p>
In particular, when developing your own Logistic Regression code for classification problems, the so-called Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more information about this at the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html" target="_self">Scikit-Learn site</a> or at the <a href="https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)" target="_self">University of California at Irvine</a>. The <a href="https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed.html" target="_self">lecture slides on dimensionality reduction have several code examples on this data set</a>.
<h3 id="___sec1" class="anchor">Part a): Write your Logistic Regression code, first step </h3>
<p>
@@ -249,6 +252,15 @@ Regression code.
You can compare your own results with those obtained using
<b>scikit-learn</b>.
<p>
As stated in the introduction, it can also be useful to study other datasets. In particular, when developing your own Logistic Regression code for classification problems, the so-called \
Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more \
information about this at the <a href="https://scikit-learn.org/stable/modules/generated/sklearn\
.datasets.load_breast_cancer.html" target="_self">Scikit-Learn site</a> or at the "University of California at Irvine":"https://archive.ics.uci.e\
du/ml/datasets/breast+cancer+wisconsin+(original)". The <a href="https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimR\
ed.html" target="_self">lecture slides on dimensionality reduction have sev\
eral code examples on this data set</a>.
<h3 id="___sec3" class="anchor">Part c): Writing your own Neural Network code </h3>
<p>
doc/Projects/2019/Project2/html/Project2.html
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@@ -112,7 +112,7 @@ MathJax.Hub.Config({
<center><b>Department of Physics, University of Oslo, Norway</b></center>
<br>
<p>
<center><h4>Oct 9, 2019</h4></center> <!-- date -->
<center><h4>Oct 17, 2019</h4></center> <!-- date -->
<br>
<h2 id="___sec0">Classification and Regression, from linear and logistic regression to neural networks </h2>
@@ -152,6 +152,9 @@ has been studied in the scientific literature. This makes it easier
for you to compare and analyze your results. It is also an essential
elements of the scientific discussion.
<p>
In particular, when developing your own Logistic Regression code for classification problems, the so-called Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more information about this at the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html" target="_blank">Scikit-Learn site</a> or at the <a href="https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)" target="_blank">University of California at Irvine</a>. The <a href="https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed.html" target="_blank">lecture slides on dimensionality reduction have several code examples on this data set</a>.
<h3 id="___sec1">Part a): Write your Logistic Regression code, first step </h3>
<p>
@@ -206,6 +209,15 @@ Regression code.
You can compare your own results with those obtained using
<b>scikit-learn</b>.
<p>
As stated in the introduction, it can also be useful to study other datasets. In particular, when developing your own Logistic Regression code for classification problems, the so-called \
Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more \
information about this at the <a href="https://scikit-learn.org/stable/modules/generated/sklearn\
.datasets.load_breast_cancer.html" target="_blank">Scikit-Learn site</a> or at the "University of California at Irvine":"https://archive.ics.uci.e\
du/ml/datasets/breast+cancer+wisconsin+(original)". The <a href="https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimR\
ed.html" target="_blank">lecture slides on dimensionality reduction have sev\
eral code examples on this data set</a>.
<h3 id="___sec3">Part c): Writing your own Neural Network code </h3>
<p>
doc/Projects/2019/Project2/ipynb/ipynb-Project2-src.tar.gz
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doc/Projects/2019/Project2/pdf/Project2.p.tex
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@@ -149,7 +149,7 @@ Project 2 on Machine Learning, deadline November 8
% --- begin date ---
\begin{center}
Oct 9, 2019
Oct 17, 2019
\end{center}
% --- end date ---
@@ -196,6 +196,8 @@ has been studied in the scientific literature. This makes it easier
for you to compare and analyze your results. It is also an essential
elements of the scientific discussion.
In particular, when developing your own Logistic Regression code for classification problems, the so-called Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more information about this at the \href{{https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html}}{Scikit-Learn site} or at the \href{{https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)}}{University of California at Irvine}. The \href{{https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed.html}}{lecture slides on dimensionality reduction have several code examples on this data set}.
\paragraph{Part a): Write your Logistic Regression code, first step.}
If you opt for the credit card data, your first task is to familiarize yourself with the data set and the scientific article.
We recommend also that you study the code example in the \href{{https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg.html}}{Logistic Regression}.
@@ -243,6 +245,15 @@ You can compare your own results with those obtained using
\textbf{scikit-learn}.
As stated in the introduction, it can also be useful to study other datasets. In particular, when developing your own Logistic Regression code for classification problems, the so-called \
Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more \
information about this at the \href{{https://scikit-learn.org/stable/modules/generated/sklearn\
.datasets.load_breast_cancer.html}}{Scikit-Learn site} or at the "University of California at Irvine":"https://archive.ics.uci.e\
du/ml/datasets/breast+cancer+wisconsin+(original)". The \href{{https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimR\
ed.html}}{lecture slides on dimensionality reduction have sev\
eral code examples on this data set}.
\paragraph{Part c): Writing your own Neural Network code.}
Your aim now, and this is the central part of this project, is to
write to your own Feed Forward Neural Network code implementing the back
doc/Projects/2019/Project2/pdf/Project2.pdf
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doc/Projects/2019/Project2/pdf/Project2.tex
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@@ -123,7 +123,7 @@ Project 2 on Machine Learning, deadline November 8
% --- begin date ---
\begin{center}
Oct 9, 2019
Oct 17, 2019
\end{center}
% --- end date ---
@@ -170,6 +170,8 @@ has been studied in the scientific literature. This makes it easier
for you to compare and analyze your results. It is also an essential
elements of the scientific discussion.
In particular, when developing your own Logistic Regression code for classification problems, the so-called Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more information about this at the \href{{https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html}}{Scikit-Learn site} or at the \href{{https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)}}{University of California at Irvine}. The \href{{https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed.html}}{lecture slides on dimensionality reduction have several code examples on this data set}.
\paragraph{Part a): Write your Logistic Regression code, first step.}
If you opt for the credit card data, your first task is to familiarize yourself with the data set and the scientific article.
We recommend also that you study the code example in the \href{{https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg.html}}{Logistic Regression}.
@@ -217,6 +219,15 @@ You can compare your own results with those obtained using
\textbf{scikit-learn}.
As stated in the introduction, it can also be useful to study other datasets. In particular, when developing your own Logistic Regression code for classification problems, the so-called \
Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more \
information about this at the \href{{https://scikit-learn.org/stable/modules/generated/sklearn\
.datasets.load_breast_cancer.html}}{Scikit-Learn site} or at the "University of California at Irvine":"https://archive.ics.uci.e\
du/ml/datasets/breast+cancer+wisconsin+(original)". The \href{{https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimR\
ed.html}}{lecture slides on dimensionality reduction have sev\
eral code examples on this data set}.
\paragraph{Part c): Writing your own Neural Network code.}
Your aim now, and this is the central part of this project, is to
write to your own Feed Forward Neural Network code implementing the back
doc/src/Projects/2019/Project2/Project2.do.txt
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@@ -22,6 +22,7 @@ The data sets that we propose here are (the default sets)
* Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are
o The credit card data set from "UCI":"https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients". This data set links to a "recent scientific article":"https://bradzzz.gitbooks.io/ga-seattle-dsi/content/dsi/dsi_05_classification_databases/2.1-lesson/assets/datasets/DefaultCreditCardClients_yeh_2009.pdf". Furthermore, in the lecture slides on "Logistic Regression":"https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg.html", you will find an example code for the Credit Card data. You could use this code as an example on how to read the data and use _Scikit-Learn_ to run a classification problem.
However, if you would like to study other data sets, feel free to
propose other sets. What we listed here are mere suggestions from our
side. If you opt for another data set, consider using a set which
@@ -29,6 +30,8 @@ has been studied in the scientific literature. This makes it easier
for you to compare and analyze your results. It is also an essential
elements of the scientific discussion.
In particular, when developing your own Logistic Regression code for classification problems, the so-called Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more information about this at the "Scikit-Learn site":"https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html" or at the "University of California at Irvine":"https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)". The "lecture slides on dimensionality reduction have several code examples on this data set":"https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed.html".
=== Part a): Write your Logistic Regression code, first step ===
If you opt for the credit card data, your first task is to familiarize yourself with the data set and the scientific article.
@@ -80,6 +83,15 @@ You can compare your own results with those obtained using
_scikit-learn_.
As stated in the introduction, it can also be useful to study other datasets. In particular, when developing your own Logistic Regression code for classification problems, the so-called \
Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more \
information about this at the "Scikit-Learn site":"https://scikit-learn.org/stable/modules/generated/sklearn\
.datasets.load_breast_cancer.html" or at the "University of California at Irvine":"https://archive.ics.uci.e\
du/ml/datasets/breast+cancer+wisconsin+(original)". The "lecture slides on dimensionality reduction have sev\
eral code examples on this data set":"https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimR\
ed.html".
=== Part c): Writing your own Neural Network code ===
Your aim now, and this is the central part of this project, is to