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Morten Hjorth-Jensen
2020-12-07 10:48:04 +01:00
parent 07f14316b2 86926860e9
commit e835e22281
20 changed files with 1395 additions and 39 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/pub/week48/html/._week48-bs000.html
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@@ -282,7 +282,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Nov 27, 2020</h4></center> <!-- date -->
<center><h4>Nov 29, 2020</h4></center> <!-- date -->
<br>
<p>
doc/pub/week48/html/._week48-bs001.html
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@@ -270,7 +270,7 @@ MathJax.Hub.Config({
<li> <b>Friday</b>: Summary of course with perspectives for future studies. <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureNovember27.mp4?vrtx=view-as-webpage" target="_self">Video of Lecture</a>.</li>
</ul>
Geron's chapter 5. Chapter 12 (sections 12.1-12.3 are the most relevant ones) of Hastie et al contains also a good discussion.
See also Geron's chapter 5. Chapter 12 (sections 12.1-12.3 are the most relevant ones) of Hastie et al contains also a good discussion.
<p>
<p>
doc/pub/week48/html/._week48-bs008.html
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@@ -270,7 +270,7 @@ We can rewrite this (see the solutions below) in terms of a convex optimization
$$
\begin{align*}
&\mathrm{min}_{\lambda}\hspace{0.2cm} \frac{1}{2}\boldsymbol{\lambda}^T\boldsymbol{P}\boldsymbol{\lambda}+\boldsymbol{q}^T\boldsymbol{\lambda},\\ \nonumber
&\mathrm{subject\hspace{0.1cm}to} \hspace{0.2cm} \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \hspace{0.2cm} \wedge \boldsymbol{A}\boldsymbol{\lambda}=f.
&\mathrm{s.t} \hspace{0.2cm} \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \hspace{0.2cm} \wedge \boldsymbol{A}\boldsymbol{\lambda}=f.
\end{align*}
$$
doc/pub/week48/html/._week48-bs013.html
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@@ -274,12 +274,14 @@ $$
\end{align*}
$$
Note: we use <b>s.t.</b> for subject to.
<b>Note</b>: we use <b>s.t.</b> for subject to.
<p>
Let us show how to perform the optmization using a simple case. Assume we want to optimize the following problem
$$
\begin{align*}
&\mathrm{min}_{x}\hspace{0.2cm} \frac{1}{2}x^2+5x+3y \\ \nonumber
&\mathrm{subject to} \\ \nonumber
&\mathrm{s.t.} \\ \nonumber
&x, y \geq 0 \\ \nonumber
&x+3y \geq 15 \\ \nonumber
&2x+5y \leq 100 \\ \nonumber
doc/pub/week48/html/week48-bs.html
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@@ -282,7 +282,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Nov 27, 2020</h4></center> <!-- date -->
<center><h4>Nov 29, 2020</h4></center> <!-- date -->
<br>
<p>
doc/pub/week48/html/week48-reveal.html
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@@ -148,7 +148,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>&nbsp;<br>
<center><h4>Nov 27, 2020</h4></center> <!-- date -->
<center><h4>Nov 29, 2020</h4></center> <!-- date -->
<br>
<p>
@@ -167,7 +167,7 @@ MathJax.Hub.Config({
</ul>
<p>
Geron's chapter 5. Chapter 12 (sections 12.1-12.3 are the most relevant ones) of Hastie et al contains also a good discussion.
See also Geron's chapter 5. Chapter 12 (sections 12.1-12.3 are the most relevant ones) of Hastie et al contains also a good discussion.
</section>
@@ -394,7 +394,7 @@ We can rewrite this (see the solutions below) in terms of a convex optimization
$$
\begin{align*}
&\mathrm{min}_{\lambda}\hspace{0.2cm} \frac{1}{2}\boldsymbol{\lambda}^T\boldsymbol{P}\boldsymbol{\lambda}+\boldsymbol{q}^T\boldsymbol{\lambda},\\ \nonumber
&\mathrm{subject\hspace{0.1cm}to} \hspace{0.2cm} \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \hspace{0.2cm} \wedge \boldsymbol{A}\boldsymbol{\lambda}=f.
&\mathrm{s.t} \hspace{0.2cm} \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \hspace{0.2cm} \wedge \boldsymbol{A}\boldsymbol{\lambda}=f.
\end{align*}
$$
<p>&nbsp;<br>
@@ -710,13 +710,15 @@ $$
$$
<p>&nbsp;<br>
Note: we use <b>s.t.</b> for subject to.
<b>Note</b>: we use <b>s.t.</b> for subject to.
<p>
Let us show how to perform the optmization using a simple case. Assume we want to optimize the following problem
<p>&nbsp;<br>
$$
\begin{align*}
&\mathrm{min}_{x}\hspace{0.2cm} \frac{1}{2}x^2+5x+3y \\ \nonumber
&\mathrm{subject to} \\ \nonumber
&\mathrm{s.t.} \\ \nonumber
&x, y \geq 0 \\ \nonumber
&x+3y \geq 15 \\ \nonumber
&2x+5y \leq 100 \\ \nonumber
doc/pub/week48/html/week48-solarized.html
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@@ -208,7 +208,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Nov 27, 2020</h4></center> <!-- date -->
<center><h4>Nov 29, 2020</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -220,7 +220,7 @@ MathJax.Hub.Config({
<li> <b>Friday</b>: Summary of course with perspectives for future studies. <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureNovember27.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a>.</li>
</ul>
Geron's chapter 5. Chapter 12 (sections 12.1-12.3 are the most relevant ones) of Hastie et al contains also a good discussion.
See also Geron's chapter 5. Chapter 12 (sections 12.1-12.3 are the most relevant ones) of Hastie et al contains also a good discussion.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -429,7 +429,7 @@ We can rewrite this (see the solutions below) in terms of a convex optimization
$$
\begin{align*}
&\mathrm{min}_{\lambda}\hspace{0.2cm} \frac{1}{2}\boldsymbol{\lambda}^T\boldsymbol{P}\boldsymbol{\lambda}+\boldsymbol{q}^T\boldsymbol{\lambda},\\ \nonumber
&\mathrm{subject\hspace{0.1cm}to} \hspace{0.2cm} \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \hspace{0.2cm} \wedge \boldsymbol{A}\boldsymbol{\lambda}=f.
&\mathrm{s.t} \hspace{0.2cm} \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \hspace{0.2cm} \wedge \boldsymbol{A}\boldsymbol{\lambda}=f.
\end{align*}
$$
@@ -736,12 +736,14 @@ $$
\end{align*}
$$
Note: we use <b>s.t.</b> for subject to.
<b>Note</b>: we use <b>s.t.</b> for subject to.
<p>
Let us show how to perform the optmization using a simple case. Assume we want to optimize the following problem
$$
\begin{align*}
&\mathrm{min}_{x}\hspace{0.2cm} \frac{1}{2}x^2+5x+3y \\ \nonumber
&\mathrm{subject to} \\ \nonumber
&\mathrm{s.t.} \\ \nonumber
&x, y \geq 0 \\ \nonumber
&x+3y \geq 15 \\ \nonumber
&2x+5y \leq 100 \\ \nonumber
doc/pub/week48/html/week48.html
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@@ -213,7 +213,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Nov 27, 2020</h4></center> <!-- date -->
<center><h4>Nov 29, 2020</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -225,7 +225,7 @@ MathJax.Hub.Config({
<li> <b>Friday</b>: Summary of course with perspectives for future studies. <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureNovember27.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a>.</li>
</ul>
Geron's chapter 5. Chapter 12 (sections 12.1-12.3 are the most relevant ones) of Hastie et al contains also a good discussion.
See also Geron's chapter 5. Chapter 12 (sections 12.1-12.3 are the most relevant ones) of Hastie et al contains also a good discussion.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -434,7 +434,7 @@ We can rewrite this (see the solutions below) in terms of a convex optimization
$$
\begin{align*}
&\mathrm{min}_{\lambda}\hspace{0.2cm} \frac{1}{2}\boldsymbol{\lambda}^T\boldsymbol{P}\boldsymbol{\lambda}+\boldsymbol{q}^T\boldsymbol{\lambda},\\ \nonumber
&\mathrm{subject\hspace{0.1cm}to} \hspace{0.2cm} \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \hspace{0.2cm} \wedge \boldsymbol{A}\boldsymbol{\lambda}=f.
&\mathrm{s.t} \hspace{0.2cm} \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \hspace{0.2cm} \wedge \boldsymbol{A}\boldsymbol{\lambda}=f.
\end{align*}
$$
@@ -741,12 +741,14 @@ $$
\end{align*}
$$
Note: we use <b>s.t.</b> for subject to.
<b>Note</b>: we use <b>s.t.</b> for subject to.
<p>
Let us show how to perform the optmization using a simple case. Assume we want to optimize the following problem
$$
\begin{align*}
&\mathrm{min}_{x}\hspace{0.2cm} \frac{1}{2}x^2+5x+3y \\ \nonumber
&\mathrm{subject to} \\ \nonumber
&\mathrm{s.t.} \\ \nonumber
&x, y \geq 0 \\ \nonumber
&x+3y \geq 15 \\ \nonumber
&2x+5y \leq 100 \\ \nonumber
doc/pub/week48/ipynb/ipynb-week48-src.tar.gz
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doc/pub/week48/ipynb/week48.ipynb
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@@ -10,7 +10,7 @@
"<!-- Author: --> \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
"Date: **Nov 27, 2020**\n",
"Date: **Nov 29, 2020**\n",
"\n",
"Copyright 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -22,7 +22,7 @@
"\n",
"* **Friday**: Summary of course with perspectives for future studies. [Video of Lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureNovember27.mp4?vrtx=view-as-webpage).\n",
"\n",
"Geron's chapter 5. Chapter 12 (sections 12.1-12.3 are the most relevant ones) of Hastie et al contains also a good discussion.\n",
"See also Geron's chapter 5. Chapter 12 (sections 12.1-12.3 are the most relevant ones) of Hastie et al contains also a good discussion.\n",
"\n",
"\n",
"## Thursday\n",
@@ -304,7 +304,7 @@
"$$\n",
"\\begin{align*}\n",
" &\\mathrm{min}_{\\lambda}\\hspace{0.2cm} \\frac{1}{2}\\boldsymbol{\\lambda}^T\\boldsymbol{P}\\boldsymbol{\\lambda}+\\boldsymbol{q}^T\\boldsymbol{\\lambda},\\\\ \\nonumber\n",
" &\\mathrm{subject\\hspace{0.1cm}to} \\hspace{0.2cm} \\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq \\boldsymbol{h} \\hspace{0.2cm} \\wedge \\boldsymbol{A}\\boldsymbol{\\lambda}=f.\n",
" &\\mathrm{s.t} \\hspace{0.2cm} \\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq \\boldsymbol{h} \\hspace{0.2cm} \\wedge \\boldsymbol{A}\\boldsymbol{\\lambda}=f.\n",
"\\end{align*}\n",
"$$"
]
@@ -650,7 +650,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: we use **s.t.** for subject to. \n",
"**Note**: we use **s.t.** for subject to. \n",
"\n",
"Let us show how to perform the optmization using a simple case. Assume we want to optimize the following problem"
]
},
@@ -661,7 +662,7 @@
"$$\n",
"\\begin{align*}\n",
" &\\mathrm{min}_{x}\\hspace{0.2cm} \\frac{1}{2}x^2+5x+3y \\\\ \\nonumber\n",
" &\\mathrm{subject to} \\\\ \\nonumber\n",
" &\\mathrm{s.t.} \\\\ \\nonumber\n",
" &x, y \\geq 0 \\\\ \\nonumber\n",
" &x+3y \\geq 15 \\\\ \\nonumber\n",
" &2x+5y \\leq 100 \\\\ \\nonumber\n",
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.
doc/src/week48/week48.do.txt
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