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update of web address

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mhjensen
2020-12-02 15:24:16 +01:00
parent 8342e4dad1
commit 86926860e9
9 changed files with 14 additions and 13 deletions
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doc/Projects/2020/Project3/html/._Project3-bs000.html
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@@ -156,7 +156,7 @@ MathJax.Hub.Config({
<center><b>Department of Physics, University of Oslo, Norway</b></center>
<br>
<p>
<center><h4>Nov 11, 2020</h4></center> <!-- date -->
<center><h4>Dec 2, 2020</h4></center> <!-- date -->
<br>
<p>
</div> <!-- end jumbotron -->
@@ -170,7 +170,7 @@ For project 3, you can propose own data sets that relate to your research intere
<ol>
<li> <a href="https://www.kaggle.com/datasets" target="_self">Kaggle</a></li>
<li> The <a href="http://archive.ics.uci.edu/ml/datasets.html" target="_self">University of California at Irvine (UCI) with its machine learning repository</a></li>
<li> The <a href="https://archive.ics.uci.edu/ml/index.php" target="_self">University of California at Irvine (UCI) with its machine learning repository</a>.</li>
</ol>
The approach to the analysis of these new data sets should follow to a large extent what you did in projects 1 and 2. That is:
doc/Projects/2020/Project3/html/Project3-bs.html
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@@ -156,7 +156,7 @@ MathJax.Hub.Config({
<center><b>Department of Physics, University of Oslo, Norway</b></center>
<br>
<p>
<center><h4>Nov 11, 2020</h4></center> <!-- date -->
<center><h4>Dec 2, 2020</h4></center> <!-- date -->
<br>
<p>
</div> <!-- end jumbotron -->
@@ -170,7 +170,7 @@ For project 3, you can propose own data sets that relate to your research intere
<ol>
<li> <a href="https://www.kaggle.com/datasets" target="_self">Kaggle</a></li>
<li> The <a href="http://archive.ics.uci.edu/ml/datasets.html" target="_self">University of California at Irvine (UCI) with its machine learning repository</a></li>
<li> The <a href="https://archive.ics.uci.edu/ml/index.php" target="_self">University of California at Irvine (UCI) with its machine learning repository</a>.</li>
</ol>
The approach to the analysis of these new data sets should follow to a large extent what you did in projects 1 and 2. That is:
doc/Projects/2020/Project3/html/Project3.html
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@@ -107,7 +107,7 @@ MathJax.Hub.Config({
<center><b>Department of Physics, University of Oslo, Norway</b></center>
<br>
<p>
<center><h4>Nov 11, 2020</h4></center> <!-- date -->
<center><h4>Dec 2, 2020</h4></center> <!-- date -->
<br>
<h1 id="___sec0">Paths for project 3 </h1>
@@ -119,7 +119,7 @@ For project 3, you can propose own data sets that relate to your research intere
<ol>
<li> <a href="https://www.kaggle.com/datasets" target="_blank">Kaggle</a></li>
<li> The <a href="http://archive.ics.uci.edu/ml/datasets.html" target="_blank">University of California at Irvine (UCI) with its machine learning repository</a></li>
<li> The <a href="https://archive.ics.uci.edu/ml/index.php" target="_blank">University of California at Irvine (UCI) with its machine learning repository</a>.</li>
</ol>
The approach to the analysis of these new data sets should follow to a large extent what you did in projects 1 and 2. That is:
doc/Projects/2020/Project3/ipynb/Project3.ipynb
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@@ -10,7 +10,7 @@
"<!-- Author: --> \n",
"**[Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html)**, Department of Physics, University of Oslo, Norway\n",
"\n",
"Date: **Nov 11, 2020**\n",
"Date: **Dec 2, 2020**\n",
"\n",
"Copyright 1999-2020, [Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html). Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -24,7 +24,7 @@
"For project 3, you can propose own data sets that relate to your research interests or just use existing data sets from say\n",
"1. [Kaggle](https://www.kaggle.com/datasets) \n",
"\n",
"2. The [University of California at Irvine (UCI) with its machine learning repository](http://archive.ics.uci.edu/ml/datasets.html)\n",
"2. The [University of California at Irvine (UCI) with its machine learning repository](https://archive.ics.uci.edu/ml/index.php).\n",
"\n",
"The approach to the analysis of these new data sets should follow to a large extent what you did in projects 1 and 2. That is:\n",
"1. Whether you end up with a regression or a classification problem, you should employ at least two of the methods we have discussed among **linear regression (including Ridge and Lasso)**, **Logistic Regression**, **Neural Networks**, **Convolution Neural Networks**, **Recurrent Neural Networks**, **Support Vector Machines** and **Decision Trees, Random Forests**, **Bagging and Boosting**. You could for example explore all of the approaches from decision trees, via bagging and voting classifiers, to random forests, boosting and finally XGboost. If you wish to venture into **convolutional neural networks** or **recurrent neural networks**, or extensions of neural networkds, feel free to do so. \n",
doc/Projects/2020/Project3/ipynb/ipynb-Project3-src.tar.gz
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doc/Projects/2020/Project3/pdf/Project3.p.tex
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@@ -149,7 +149,7 @@ Project 3 on Machine Learning, deadline December 14
% --- begin date ---
\begin{center}
Nov 11, 2020
Dec 2, 2020
\end{center}
% --- end date ---
@@ -164,7 +164,7 @@ For project 3, you can propose own data sets that relate to your research intere
\begin{enumerate}
\item \href{{https://www.kaggle.com/datasets}}{Kaggle}
\item The \href{{http://archive.ics.uci.edu/ml/datasets.html}}{University of California at Irvine (UCI) with its machine learning repository}
\item The \href{{https://archive.ics.uci.edu/ml/index.php}}{University of California at Irvine (UCI) with its machine learning repository}.
\end{enumerate}
\noindent
doc/Projects/2020/Project3/pdf/Project3.pdf
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doc/Projects/2020/Project3/pdf/Project3.tex
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@@ -123,7 +123,7 @@ Project 3 on Machine Learning, deadline December 14
% --- begin date ---
\begin{center}
Nov 11, 2020
Dec 2, 2020
\end{center}
% --- end date ---
@@ -138,7 +138,7 @@ For project 3, you can propose own data sets that relate to your research intere
\begin{enumerate}
\item \href{{https://www.kaggle.com/datasets}}{Kaggle}
\item The \href{{http://archive.ics.uci.edu/ml/datasets.html}}{University of California at Irvine (UCI) with its machine learning repository}
\item The \href{{https://archive.ics.uci.edu/ml/index.php}}{University of California at Irvine (UCI) with its machine learning repository}.
\end{enumerate}
\noindent
doc/src/Projects/2020/Project3/Project3.do.txt
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@@ -9,7 +9,8 @@ DATE: today
For project 3, you can propose own data sets that relate to your research interests or just use existing data sets from say
o "Kaggle":"https://www.kaggle.com/datasets"
o The "University of California at Irvine (UCI) with its machine learning repository":"http://archive.ics.uci.edu/ml/datasets.html"
o The "University of California at Irvine (UCI) with its machine learning repository":"https://archive.ics.uci.edu/ml/index.php".
The approach to the analysis of these new data sets should follow to a large extent what you did in projects 1 and 2. That is:
o Whether you end up with a regression or a classification problem, you should employ at least two of the methods we have discussed among _linear regression (including Ridge and Lasso)_, _Logistic Regression_, _Neural Networks_, _Convolution Neural Networks_, _Recurrent Neural Networks_, _Support Vector Machines_ and _Decision Trees, Random Forests_, _Bagging and Boosting_. You could for example explore all of the approaches from decision trees, via bagging and voting classifiers, to random forests, boosting and finally XGboost. If you wish to venture into _convolutional neural networks_ or _recurrent neural networks_, or extensions of neural networkds, feel free to do so.