lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update on projects and schedule (not yet finished)

Browse Source
This commit is contained in:
mhjensen
2018-08-06 16:05:08 +02:00
parent cae8f54fb8
commit 34fb6d4b38
12 changed files with 1825 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/Projects/2018/Project1/html/Project1-bs.html
+242
View File
@@ -0,0 +1,242 @@
<!--
Automatically generated HTML file from DocOnce source
(https://github.com/hplgit/doconce/)
-->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
<meta name="description" content="Project 1 on Machine Learning, deadline October 1">
<title>Project 1 on Machine Learning, deadline October 1</title>
<!-- Bootstrap style: bootstrap -->
<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
<!-- not necessary
<link href="https://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
-->
<style type="text/css">
/* Add scrollbar to dropdown menus in bootstrap navigation bar */
.dropdown-menu {
height: auto;
max-height: 400px;
overflow-x: hidden;
}
/* Adds an invisible element before each target to offset for the navigation
bar */
.anchor::before {
content:"";
display:block;
height:50px; /* fixed header height for style bootstrap */
margin:-50px 0 0; /* negative fixed header height */
}
</style>
</head>
<!-- tocinfo
{'highest level': 2,
'sections': [('Regression analysis and classification', 2, None, '___sec0'),
('Introduction', 3, None, '___sec1'),
('Part a): The data', 3, None, '___sec2'),
('Background literature', 2, None, '___sec3'),
('Introduction to numerical projects', 2, None, '___sec4'),
('Format for electronic delivery of report and programs',
2,
None,
'___sec5'),
('Software and needed installations', 2, None, '___sec6')]}
end of tocinfo -->
<body>
<!-- Bootstrap navigation bar -->
<div class="navbar navbar-default navbar-fixed-top">
<div class="navbar-header">
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-responsive-collapse">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="Project1-bs.html">Project 1 on Machine Learning, deadline October 1</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
<ul class="nav navbar-nav navbar-right">
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
<ul class="dropdown-menu">
<!-- navigation toc: --> <li><a href="#___sec0" style="font-size: 80%;"><b>Regression analysis and classification</b></a></li>
<!-- navigation toc: --> <li><a href="#___sec1" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Introduction</a></li>
<!-- navigation toc: --> <li><a href="#___sec2" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Part a): The data</a></li>
<!-- navigation toc: --> <li><a href="#___sec3" style="font-size: 80%;"><b>Background literature</b></a></li>
<!-- navigation toc: --> <li><a href="#___sec4" style="font-size: 80%;"><b>Introduction to numerical projects</b></a></li>
<!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;"><b>Format for electronic delivery of report and programs</b></a></li>
<!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;"><b>Software and needed installations</b></a></li>
</ul>
</li>
</ul>
</div>
</div>
</div> <!-- end of navigation bar -->
<div class="container">
<p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p> <!-- add vertical space -->
<a name="part0000"></a>
<!-- ------------------- main content ---------------------- -->
<div class="jumbotron">
<center><h1>Project 1 on Machine Learning, deadline October 1</h1></center> <!-- document title -->
<p>
<!-- author(s): <a href="http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html" target="_self">Data Analysis and Machine Learning FYS-STK3155/FYS4155</a> -->
<center>
<b><a href="http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html" target="_self">Data Analysis and Machine Learning FYS-STK3155/FYS4155</a></b>
</center>
<p>
<!-- institution -->
<center><b>Department of Physics, University of Oslo, Norway</b></center>
<br>
<p>
<center><h4>May 2018</h4></center> <!-- date -->
<br>
<p>
</div> <!-- end jumbotron -->
<h2 id="___sec0" class="anchor">Regression analysis and classification </h2>
<h3 id="___sec1" class="anchor">Introduction </h3>
<h3 id="___sec2" class="anchor">Part a): The data </h3>
<h2 id="___sec3" class="anchor">Background literature </h2>
<h2 id="___sec4" class="anchor">Introduction to numerical projects </h2>
<p>
Here follows a brief recipe and recommendation on how to write a report for each
project.
<ul>
<li> Give a short description of the nature of the problem and the eventual numerical methods you have used.</li>
<li> Describe the algorithm you have used and/or developed. Here you may find it convenient to use pseudocoding. In many cases you can describe the algorithm in the program itself.</li>
<li> Include the source code of your program. Comment your program properly.</li>
<li> If possible, try to find analytic solutions, or known limits in order to test your program when developing the code.</li>
<li> Include your results either in figure form or in a table. Remember to label your results. All tables and figures should have relevant captions and labels on the axes.</li>
<li> Try to evaluate the reliabilty and numerical stability/precision of your results. If possible, include a qualitative and/or quantitative discussion of the numerical stability, eventual loss of precision etc.</li>
<li> Try to give an interpretation of you results in your answers to the problems.</li>
<li> Critique: if possible include your comments and reflections about the exercise, whether you felt you learnt something, ideas for improvements and other thoughts you've made when solving the exercise. We wish to keep this course at the interactive level and your comments can help us improve it.</li>
<li> Try to establish a practice where you log your work at the computerlab. You may find such a logbook very handy at later stages in your work, especially when you don't properly remember what a previous test version of your program did. Here you could also record the time spent on solving the exercise, various algorithms you may have tested or other topics which you feel worthy of mentioning.</li>
</ul>
<h2 id="___sec5" class="anchor">Format for electronic delivery of report and programs </h2>
<p>
The preferred format for the report is a PDF file. You can also use DOC or postscript formats or as an ipython notebook file. As programming language we prefer that you choose between C/C++, Fortran2008 or Python. The following prescription should be followed when preparing the report:
<ul>
<li> Use Devilry to hand in your projects, log in at <a href="http://devilry.ifi.uio.no" target="_self"><tt>http://devilry.ifi.uio.no</tt></a> with your normal UiO username and password and choose either 'fysstk3155' or 'fysstk4155'. There you can load up the files within the deadline.</li>
<li> Upload <b>only</b> the report file! For the source code file(s) you have developed please provide us with your link to your github domain. The report file should include all of your discussions and a list of the codes you have developed. Do not include library files which are available at the course homepage, unless you have made specific changes to them.</li>
<li> In your git repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters.</li>
<li> In this and all later projects, you should include tests (for example unit tests) of your code(s).</li>
<li> Comments from us on your projects, approval or not, corrections to be made etc can be found under your Devilry domain and are only visible to you and the teachers of the course.</li>
</ul>
Finally,
we encourage you to work two and two together. Optimal working groups consist of
2-3 students. You can then hand in a common report.
<h2 id="___sec6" class="anchor">Software and needed installations </h2>
<p>
If you have Python installed (we recommend Python3) and you feel pretty familiar with installing different packages,
we recommend that you install the following Python packages via <b>pip</b> as
<ol>
<li> pip install numpy scipy matplotlib ipython scikit-learn tensorflow sympy pandas pillow</li>
</ol>
For Python3, replace <b>pip</b> with <b>pip3</b>.
<p>
See below for a discussion of <b>tensorflow</b> and <b>scikit-learn</b>.
<p>
For OSX users we recommend also, after having installed Xcode, to install <b>brew</b>. Brew allows
for a seamless installation of additional software via for example
<ol>
<li> brew install python3</li>
</ol>
For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution
you can use <b>pip</b> as well and simply install Python as
<ol>
<li> sudo apt-get install python3 (or python for python2.7)</li>
</ol>
etc etc.
<p>
If you don't want to install various Python packages with their dependencies separately, we recommend two widely used distrubutions which set up all relevant dependencies for Python, namely
<ol>
<li> <a href="https://docs.anaconda.com/" target="_self">Anaconda</a> Anaconda is an open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system <b>conda</b></li>
<li> <a href="https://www.enthought.com/product/canopy/" target="_self">Enthought canopy</a> is a Python distribution for scientific and analytic computing distribution and analysis environment, available for free and under a commercial license.</li>
</ol>
Popular software packages written in Python for ML are
<ul>
<li> <a href="http://scikit-learn.org/stable/" target="_self">Scikit-learn</a>,</li>
<li> <a href="https://www.tensorflow.org/" target="_self">Tensorflow</a>,</li>
<li> <a href="http://pytorch.org/" target="_self">PyTorch</a> and</li>
<li> <a href="https://keras.io/" target="_self">Keras</a>.</li>
</ul>
These are all freely available at their respective GitHub sites. They
encompass communities of developers in the thousands or more. And the number
of code developers and contributors keeps increasing.
<p>
<!-- navigation buttons at the bottom of the page -->
<ul class="pagination">
<li class="active"><a href="._Project1-bs000.html">1</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
</div> <!-- end container -->
<!-- include javascript, jQuery *first* -->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
<!-- Bootstrap footer
<footer>
<a href="http://..."><img width="250" align=right src="http://..."></a>
</footer>
-->
<center style="font-size:80%">
<!-- copyright --> &copy; 1999-2018, "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
</center>
</body>
</html>
doc/Projects/2018/Project1/html/Project1.html
+186
View File
@@ -0,0 +1,186 @@
<!--
Automatically generated HTML file from DocOnce source
(https://github.com/hplgit/doconce/)
-->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
<meta name="description" content="Project 1 on Machine Learning, deadline October 1">
<title>Project 1 on Machine Learning, deadline October 1</title>
<style type="text/css">
/* bloodish style */
body {
font-family: Helvetica, Verdana, Arial, Sans-serif;
color: #404040;
background: #ffffff;
}
h1 { font-size: 1.8em; color: #8A0808; }
h2 { font-size: 1.6em; color: #8A0808; }
h3 { font-size: 1.4em; color: #8A0808; }
h4 { color: #8A0808; }
a { color: #8A0808; text-decoration:none; }
tt { font-family: "Courier New", Courier; }
/* pre style removed because it will interfer with pygments */
p { text-indent: 0px; }
hr { border: 0; width: 80%; border-bottom: 1px solid #aaa}
p.caption { width: 80%; font-style: normal; text-align: left; }
hr.figure { border: 0; width: 80%; border-bottom: 1px solid #aaa}
div { text-align: justify; text-justify: inter-word; }
</style>
</head>
<!-- tocinfo
{'highest level': 2,
'sections': [('Regression analysis and classification', 2, None, '___sec0'),
('Introduction', 3, None, '___sec1'),
('Part a): The data', 3, None, '___sec2'),
('Background literature', 2, None, '___sec3'),
('Introduction to numerical projects', 2, None, '___sec4'),
('Format for electronic delivery of report and programs',
2,
None,
'___sec5'),
('Software and needed installations', 2, None, '___sec6')]}
end of tocinfo -->
<body>
<!-- ------------------- main content ---------------------- -->
<center><h1>Project 1 on Machine Learning, deadline October 1</h1></center> <!-- document title -->
<p>
<!-- author(s): <a href="http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html" target="_blank">Data Analysis and Machine Learning FYS-STK3155/FYS4155</a> -->
<center>
<b><a href="http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html" target="_blank">Data Analysis and Machine Learning FYS-STK3155/FYS4155</a></b>
</center>
<p>
<!-- institution -->
<center><b>Department of Physics, University of Oslo, Norway</b></center>
<br>
<p>
<center><h4>May 2018</h4></center> <!-- date -->
<br>
<h2 id="___sec0">Regression analysis and classification </h2>
<h3 id="___sec1">Introduction </h3>
<h3 id="___sec2">Part a): The data </h3>
<h2 id="___sec3">Background literature </h2>
<h2 id="___sec4">Introduction to numerical projects </h2>
<p>
Here follows a brief recipe and recommendation on how to write a report for each
project.
<ul>
<li> Give a short description of the nature of the problem and the eventual numerical methods you have used.</li>
<li> Describe the algorithm you have used and/or developed. Here you may find it convenient to use pseudocoding. In many cases you can describe the algorithm in the program itself.</li>
<li> Include the source code of your program. Comment your program properly.</li>
<li> If possible, try to find analytic solutions, or known limits in order to test your program when developing the code.</li>
<li> Include your results either in figure form or in a table. Remember to label your results. All tables and figures should have relevant captions and labels on the axes.</li>
<li> Try to evaluate the reliabilty and numerical stability/precision of your results. If possible, include a qualitative and/or quantitative discussion of the numerical stability, eventual loss of precision etc.</li>
<li> Try to give an interpretation of you results in your answers to the problems.</li>
<li> Critique: if possible include your comments and reflections about the exercise, whether you felt you learnt something, ideas for improvements and other thoughts you've made when solving the exercise. We wish to keep this course at the interactive level and your comments can help us improve it.</li>
<li> Try to establish a practice where you log your work at the computerlab. You may find such a logbook very handy at later stages in your work, especially when you don't properly remember what a previous test version of your program did. Here you could also record the time spent on solving the exercise, various algorithms you may have tested or other topics which you feel worthy of mentioning.</li>
</ul>
<h2 id="___sec5">Format for electronic delivery of report and programs </h2>
<p>
The preferred format for the report is a PDF file. You can also use DOC or postscript formats or as an ipython notebook file. As programming language we prefer that you choose between C/C++, Fortran2008 or Python. The following prescription should be followed when preparing the report:
<ul>
<li> Use Devilry to hand in your projects, log in at <a href="http://devilry.ifi.uio.no" target="_blank"><tt>http://devilry.ifi.uio.no</tt></a> with your normal UiO username and password and choose either 'fysstk3155' or 'fysstk4155'. There you can load up the files within the deadline.</li>
<li> Upload <b>only</b> the report file! For the source code file(s) you have developed please provide us with your link to your github domain. The report file should include all of your discussions and a list of the codes you have developed. Do not include library files which are available at the course homepage, unless you have made specific changes to them.</li>
<li> In your git repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters.</li>
<li> In this and all later projects, you should include tests (for example unit tests) of your code(s).</li>
<li> Comments from us on your projects, approval or not, corrections to be made etc can be found under your Devilry domain and are only visible to you and the teachers of the course.</li>
</ul>
Finally,
we encourage you to work two and two together. Optimal working groups consist of
2-3 students. You can then hand in a common report.
<h2 id="___sec6">Software and needed installations </h2>
<p>
If you have Python installed (we recommend Python3) and you feel pretty familiar with installing different packages,
we recommend that you install the following Python packages via <b>pip</b> as
<ol>
<li> pip install numpy scipy matplotlib ipython scikit-learn tensorflow sympy pandas pillow</li>
</ol>
For Python3, replace <b>pip</b> with <b>pip3</b>.
<p>
See below for a discussion of <b>tensorflow</b> and <b>scikit-learn</b>.
<p>
For OSX users we recommend also, after having installed Xcode, to install <b>brew</b>. Brew allows
for a seamless installation of additional software via for example
<ol>
<li> brew install python3</li>
</ol>
For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution
you can use <b>pip</b> as well and simply install Python as
<ol>
<li> sudo apt-get install python3 (or python for python2.7)</li>
</ol>
etc etc.
<p>
If you don't want to install various Python packages with their dependencies separately, we recommend two widely used distrubutions which set up all relevant dependencies for Python, namely
<ol>
<li> <a href="https://docs.anaconda.com/" target="_blank">Anaconda</a> Anaconda is an open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system <b>conda</b></li>
<li> <a href="https://www.enthought.com/product/canopy/" target="_blank">Enthought canopy</a> is a Python distribution for scientific and analytic computing distribution and analysis environment, available for free and under a commercial license.</li>
</ol>
Popular software packages written in Python for ML are
<ul>
<li> <a href="http://scikit-learn.org/stable/" target="_blank">Scikit-learn</a>,</li>
<li> <a href="https://www.tensorflow.org/" target="_blank">Tensorflow</a>,</li>
<li> <a href="http://pytorch.org/" target="_blank">PyTorch</a> and</li>
<li> <a href="https://keras.io/" target="_blank">Keras</a>.</li>
</ul>
These are all freely available at their respective GitHub sites. They
encompass communities of developers in the thousands or more. And the number
of code developers and contributors keeps increasing.
<!-- ------------------- end of main content --------------- -->
<center style="font-size:80%">
<!-- copyright --> &copy; 1999-2018, "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
</center>
</body>
</html>
doc/Projects/2018/Project1/ipynb/ipynb-Project1-src.tar.gz
BIN
View File
Binary file not shown.
doc/Projects/2018/Project1/pdf/Project1.p.tex
+277
View File
@@ -0,0 +1,277 @@
%%
%% Automatically generated file from DocOnce source
%% (https://github.com/hplgit/doconce/)
%%
%%
% #ifdef PTEX2TEX_EXPLANATION
%%
%% The file follows the ptex2tex extended LaTeX format, see
%% ptex2tex: http://code.google.com/p/ptex2tex/
%%
%% Run
%% ptex2tex myfile
%% or
%% doconce ptex2tex myfile
%%
%% to turn myfile.p.tex into an ordinary LaTeX file myfile.tex.
%% (The ptex2tex program: http://code.google.com/p/ptex2tex)
%% Many preprocess options can be added to ptex2tex or doconce ptex2tex
%%
%% ptex2tex -DMINTED myfile
%% doconce ptex2tex myfile envir=minted
%%
%% ptex2tex will typeset code environments according to a global or local
%% .ptex2tex.cfg configure file. doconce ptex2tex will typeset code
%% according to options on the command line (just type doconce ptex2tex to
%% see examples). If doconce ptex2tex has envir=minted, it enables the
%% minted style without needing -DMINTED.
% #endif
% #define PREAMBLE
% #ifdef PREAMBLE
%-------------------- begin preamble ----------------------
\documentclass[%
oneside, % oneside: electronic viewing, twoside: printing
final, % draft: marks overfull hboxes, figures with paths
10pt]{article}
\listfiles % print all files needed to compile this document
\usepackage{relsize,makeidx,color,setspace,amsmath,amsfonts,amssymb}
\usepackage[table]{xcolor}
\usepackage{bm,ltablex,microtype}
\usepackage[pdftex]{graphicx}
\usepackage[T1]{fontenc}
%\usepackage[latin1]{inputenc}
\usepackage{ucs}
\usepackage[utf8x]{inputenc}
\usepackage{lmodern} % Latin Modern fonts derived from Computer Modern
% Hyperlinks in PDF:
\definecolor{linkcolor}{rgb}{0,0,0.4}
\usepackage{hyperref}
\hypersetup{
breaklinks=true,
colorlinks=true,
linkcolor=linkcolor,
urlcolor=linkcolor,
citecolor=black,
filecolor=black,
%filecolor=blue,
pdfmenubar=true,
pdftoolbar=true,
bookmarksdepth=3 % Uncomment (and tweak) for PDF bookmarks with more levels than the TOC
}
%\hyperbaseurl{} % hyperlinks are relative to this root
\setcounter{tocdepth}{2} % levels in table of contents
% --- fancyhdr package for fancy headers ---
\usepackage{fancyhdr}
\fancyhf{} % sets both header and footer to nothing
\renewcommand{\headrulewidth}{0pt}
\fancyfoot[LE,RO]{\thepage}
% Ensure copyright on titlepage (article style) and chapter pages (book style)
\fancypagestyle{plain}{
\fancyhf{}
\fancyfoot[C]{{\footnotesize \copyright\ 1999-2018, "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}}
% \renewcommand{\footrulewidth}{0mm}
\renewcommand{\headrulewidth}{0mm}
}
% Ensure copyright on titlepages with \thispagestyle{empty}
\fancypagestyle{empty}{
\fancyhf{}
\fancyfoot[C]{{\footnotesize \copyright\ 1999-2018, "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}}
\renewcommand{\footrulewidth}{0mm}
\renewcommand{\headrulewidth}{0mm}
}
\pagestyle{fancy}
% prevent orhpans and widows
\clubpenalty = 10000
\widowpenalty = 10000
% --- end of standard preamble for documents ---
% insert custom LaTeX commands...
\raggedbottom
\makeindex
\usepackage[totoc]{idxlayout} % for index in the toc
\usepackage[nottoc]{tocbibind} % for references/bibliography in the toc
%-------------------- end preamble ----------------------
\begin{document}
% matching end for #ifdef PREAMBLE
% #endif
\newcommand{\exercisesection}[1]{\subsection*{#1}}
% ------------------- main content ----------------------
% ----------------- title -------------------------
\thispagestyle{empty}
\begin{center}
{\LARGE\bf
\begin{spacing}{1.25}
Project 1 on Machine Learning, deadline October 1
\end{spacing}
}
\end{center}
% ----------------- author(s) -------------------------
\begin{center}
{\bf \href{{http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html}}{Data Analysis and Machine Learning FYS-STK3155/FYS4155}}
\end{center}
\begin{center}
% List of all institutions:
\centerline{{\small Department of Physics, University of Oslo, Norway}}
\end{center}
% ----------------- end author(s) -------------------------
% --- begin date ---
\begin{center}
May 2018
\end{center}
% --- end date ---
\vspace{1cm}
\subsection{Regression analysis and classification}
\paragraph{Introduction.}
\paragraph{Part a): The data.}
\subsection{Background literature}
\subsection{Introduction to numerical projects}
Here follows a brief recipe and recommendation on how to write a report for each
project.
\begin{itemize}
\item Give a short description of the nature of the problem and the eventual numerical methods you have used.
\item Describe the algorithm you have used and/or developed. Here you may find it convenient to use pseudocoding. In many cases you can describe the algorithm in the program itself.
\item Include the source code of your program. Comment your program properly.
\item If possible, try to find analytic solutions, or known limits in order to test your program when developing the code.
\item Include your results either in figure form or in a table. Remember to label your results. All tables and figures should have relevant captions and labels on the axes.
\item Try to evaluate the reliabilty and numerical stability/precision of your results. If possible, include a qualitative and/or quantitative discussion of the numerical stability, eventual loss of precision etc.
\item Try to give an interpretation of you results in your answers to the problems.
\item Critique: if possible include your comments and reflections about the exercise, whether you felt you learnt something, ideas for improvements and other thoughts you've made when solving the exercise. We wish to keep this course at the interactive level and your comments can help us improve it.
\item Try to establish a practice where you log your work at the computerlab. You may find such a logbook very handy at later stages in your work, especially when you don't properly remember what a previous test version of your program did. Here you could also record the time spent on solving the exercise, various algorithms you may have tested or other topics which you feel worthy of mentioning.
\end{itemize}
\noindent
\subsection{Format for electronic delivery of report and programs}
The preferred format for the report is a PDF file. You can also use DOC or postscript formats or as an ipython notebook file. As programming language we prefer that you choose between C/C++, Fortran2008 or Python. The following prescription should be followed when preparing the report:
\begin{itemize}
\item Use Devilry to hand in your projects, log in at \href{{http://devilry.ifi.uio.no}}{\nolinkurl{http://devilry.ifi.uio.no}} with your normal UiO username and password and choose either 'fysstk3155' or 'fysstk4155'. There you can load up the files within the deadline.
\item Upload \textbf{only} the report file! For the source code file(s) you have developed please provide us with your link to your github domain. The report file should include all of your discussions and a list of the codes you have developed. Do not include library files which are available at the course homepage, unless you have made specific changes to them.
\item In your git repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters.
\item In this and all later projects, you should include tests (for example unit tests) of your code(s).
\item Comments from us on your projects, approval or not, corrections to be made etc can be found under your Devilry domain and are only visible to you and the teachers of the course.
\end{itemize}
\noindent
Finally,
we encourage you to work two and two together. Optimal working groups consist of
2-3 students. You can then hand in a common report.
\subsection{Software and needed installations}
If you have Python installed (we recommend Python3) and you feel pretty familiar with installing different packages,
we recommend that you install the following Python packages via \textbf{pip} as
\begin{enumerate}
\item pip install numpy scipy matplotlib ipython scikit-learn tensorflow sympy pandas pillow
\end{enumerate}
\noindent
For Python3, replace \textbf{pip} with \textbf{pip3}.
See below for a discussion of \textbf{tensorflow} and \textbf{scikit-learn}.
For OSX users we recommend also, after having installed Xcode, to install \textbf{brew}. Brew allows
for a seamless installation of additional software via for example
\begin{enumerate}
\item brew install python3
\end{enumerate}
\noindent
For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution
you can use \textbf{pip} as well and simply install Python as
\begin{enumerate}
\item sudo apt-get install python3 (or python for python2.7)
\end{enumerate}
\noindent
etc etc.
If you don't want to install various Python packages with their dependencies separately, we recommend two widely used distrubutions which set up all relevant dependencies for Python, namely
\begin{enumerate}
\item \href{{https://docs.anaconda.com/}}{Anaconda} Anaconda is an open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system \textbf{conda}
\item \href{{https://www.enthought.com/product/canopy/}}{Enthought canopy} is a Python distribution for scientific and analytic computing distribution and analysis environment, available for free and under a commercial license.
\end{enumerate}
\noindent
Popular software packages written in Python for ML are
\begin{itemize}
\item \href{{http://scikit-learn.org/stable/}}{Scikit-learn},
\item \href{{https://www.tensorflow.org/}}{Tensorflow},
\item \href{{http://pytorch.org/}}{PyTorch} and
\item \href{{https://keras.io/}}{Keras}.
\end{itemize}
\noindent
These are all freely available at their respective GitHub sites. They
encompass communities of developers in the thousands or more. And the number
of code developers and contributors keeps increasing.
% ------------------- end of main content ---------------
% #ifdef PREAMBLE
\end{document}
% #endif
doc/Projects/2018/Project1/pdf/Project1.pdf
BIN
View File
Binary file not shown.
doc/Projects/2018/Project1/pdf/Project1.tex
+249
View File
@@ -0,0 +1,249 @@
%%
%% Automatically generated file from DocOnce source
%% (https://github.com/hplgit/doconce/)
%%
%%
%-------------------- begin preamble ----------------------
\documentclass[%
oneside, % oneside: electronic viewing, twoside: printing
final, % draft: marks overfull hboxes, figures with paths
10pt]{article}
\listfiles % print all files needed to compile this document
\usepackage{relsize,makeidx,color,setspace,amsmath,amsfonts,amssymb}
\usepackage[table]{xcolor}
\usepackage{bm,ltablex,microtype}
\usepackage[pdftex]{graphicx}
\usepackage[T1]{fontenc}
%\usepackage[latin1]{inputenc}
\usepackage{ucs}
\usepackage[utf8x]{inputenc}
\usepackage{lmodern} % Latin Modern fonts derived from Computer Modern
% Hyperlinks in PDF:
\definecolor{linkcolor}{rgb}{0,0,0.4}
\usepackage{hyperref}
\hypersetup{
breaklinks=true,
colorlinks=true,
linkcolor=linkcolor,
urlcolor=linkcolor,
citecolor=black,
filecolor=black,
%filecolor=blue,
pdfmenubar=true,
pdftoolbar=true,
bookmarksdepth=3 % Uncomment (and tweak) for PDF bookmarks with more levels than the TOC
}
%\hyperbaseurl{} % hyperlinks are relative to this root
\setcounter{tocdepth}{2} % levels in table of contents
% --- fancyhdr package for fancy headers ---
\usepackage{fancyhdr}
\fancyhf{} % sets both header and footer to nothing
\renewcommand{\headrulewidth}{0pt}
\fancyfoot[LE,RO]{\thepage}
% Ensure copyright on titlepage (article style) and chapter pages (book style)
\fancypagestyle{plain}{
\fancyhf{}
\fancyfoot[C]{{\footnotesize \copyright\ 1999-2018, "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}}
% \renewcommand{\footrulewidth}{0mm}
\renewcommand{\headrulewidth}{0mm}
}
% Ensure copyright on titlepages with \thispagestyle{empty}
\fancypagestyle{empty}{
\fancyhf{}
\fancyfoot[C]{{\footnotesize \copyright\ 1999-2018, "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}}
\renewcommand{\footrulewidth}{0mm}
\renewcommand{\headrulewidth}{0mm}
}
\pagestyle{fancy}
% prevent orhpans and widows
\clubpenalty = 10000
\widowpenalty = 10000
% --- end of standard preamble for documents ---
% insert custom LaTeX commands...
\raggedbottom
\makeindex
\usepackage[totoc]{idxlayout} % for index in the toc
\usepackage[nottoc]{tocbibind} % for references/bibliography in the toc
%-------------------- end preamble ----------------------
\begin{document}
% matching end for #ifdef PREAMBLE
\newcommand{\exercisesection}[1]{\subsection*{#1}}
% ------------------- main content ----------------------
% ----------------- title -------------------------
\thispagestyle{empty}
\begin{center}
{\LARGE\bf
\begin{spacing}{1.25}
Project 1 on Machine Learning, deadline October 1
\end{spacing}
}
\end{center}
% ----------------- author(s) -------------------------
\begin{center}
{\bf \href{{http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html}}{Data Analysis and Machine Learning FYS-STK3155/FYS4155}}
\end{center}
\begin{center}
% List of all institutions:
\centerline{{\small Department of Physics, University of Oslo, Norway}}
\end{center}
% ----------------- end author(s) -------------------------
% --- begin date ---
\begin{center}
May 2018
\end{center}
% --- end date ---
\vspace{1cm}
\subsection*{Regression analysis and classification}
\paragraph{Introduction.}
\paragraph{Part a): The data.}
\subsection*{Background literature}
\subsection*{Introduction to numerical projects}
Here follows a brief recipe and recommendation on how to write a report for each
project.
\begin{itemize}
\item Give a short description of the nature of the problem and the eventual numerical methods you have used.
\item Describe the algorithm you have used and/or developed. Here you may find it convenient to use pseudocoding. In many cases you can describe the algorithm in the program itself.
\item Include the source code of your program. Comment your program properly.
\item If possible, try to find analytic solutions, or known limits in order to test your program when developing the code.
\item Include your results either in figure form or in a table. Remember to label your results. All tables and figures should have relevant captions and labels on the axes.
\item Try to evaluate the reliabilty and numerical stability/precision of your results. If possible, include a qualitative and/or quantitative discussion of the numerical stability, eventual loss of precision etc.
\item Try to give an interpretation of you results in your answers to the problems.
\item Critique: if possible include your comments and reflections about the exercise, whether you felt you learnt something, ideas for improvements and other thoughts you've made when solving the exercise. We wish to keep this course at the interactive level and your comments can help us improve it.
\item Try to establish a practice where you log your work at the computerlab. You may find such a logbook very handy at later stages in your work, especially when you don't properly remember what a previous test version of your program did. Here you could also record the time spent on solving the exercise, various algorithms you may have tested or other topics which you feel worthy of mentioning.
\end{itemize}
\noindent
\subsection*{Format for electronic delivery of report and programs}
The preferred format for the report is a PDF file. You can also use DOC or postscript formats or as an ipython notebook file. As programming language we prefer that you choose between C/C++, Fortran2008 or Python. The following prescription should be followed when preparing the report:
\begin{itemize}
\item Use Devilry to hand in your projects, log in at \href{{http://devilry.ifi.uio.no}}{\nolinkurl{http://devilry.ifi.uio.no}} with your normal UiO username and password and choose either 'fysstk3155' or 'fysstk4155'. There you can load up the files within the deadline.
\item Upload \textbf{only} the report file! For the source code file(s) you have developed please provide us with your link to your github domain. The report file should include all of your discussions and a list of the codes you have developed. Do not include library files which are available at the course homepage, unless you have made specific changes to them.
\item In your git repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters.
\item In this and all later projects, you should include tests (for example unit tests) of your code(s).
\item Comments from us on your projects, approval or not, corrections to be made etc can be found under your Devilry domain and are only visible to you and the teachers of the course.
\end{itemize}
\noindent
Finally,
we encourage you to work two and two together. Optimal working groups consist of
2-3 students. You can then hand in a common report.
\subsection*{Software and needed installations}
If you have Python installed (we recommend Python3) and you feel pretty familiar with installing different packages,
we recommend that you install the following Python packages via \textbf{pip} as
\begin{enumerate}
\item pip install numpy scipy matplotlib ipython scikit-learn tensorflow sympy pandas pillow
\end{enumerate}
\noindent
For Python3, replace \textbf{pip} with \textbf{pip3}.
See below for a discussion of \textbf{tensorflow} and \textbf{scikit-learn}.
For OSX users we recommend also, after having installed Xcode, to install \textbf{brew}. Brew allows
for a seamless installation of additional software via for example
\begin{enumerate}
\item brew install python3
\end{enumerate}
\noindent
For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution
you can use \textbf{pip} as well and simply install Python as
\begin{enumerate}
\item sudo apt-get install python3 (or python for python2.7)
\end{enumerate}
\noindent
etc etc.
If you don't want to install various Python packages with their dependencies separately, we recommend two widely used distrubutions which set up all relevant dependencies for Python, namely
\begin{enumerate}
\item \href{{https://docs.anaconda.com/}}{Anaconda} Anaconda is an open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system \textbf{conda}
\item \href{{https://www.enthought.com/product/canopy/}}{Enthought canopy} is a Python distribution for scientific and analytic computing distribution and analysis environment, available for free and under a commercial license.
\end{enumerate}
\noindent
Popular software packages written in Python for ML are
\begin{itemize}
\item \href{{http://scikit-learn.org/stable/}}{Scikit-learn},
\item \href{{https://www.tensorflow.org/}}{Tensorflow},
\item \href{{http://pytorch.org/}}{PyTorch} and
\item \href{{https://keras.io/}}{Keras}.
\end{itemize}
\noindent
These are all freely available at their respective GitHub sites. They
encompass communities of developers in the thousands or more. And the number
of code developers and contributors keeps increasing.
% ------------------- end of main content ---------------
\end{document}
doc/src/Projects/2018/Project1/Project1.do.txt
+100
View File
@@ -0,0 +1,100 @@
TITLE: Project 1 on Machine Learning, deadline October 1
AUTHOR: "Data Analysis and Machine Learning FYS-STK3155/FYS4155":"http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html" {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo, Norway
DATE: May 2018
===== Regression analysis and classification =====
=== Introduction ===
=== Part a): The data ===
===== Background literature =====
===== Introduction to numerical projects =====
Here follows a brief recipe and recommendation on how to write a report for each
project.
* Give a short description of the nature of the problem and the eventual numerical methods you have used.
* Describe the algorithm you have used and/or developed. Here you may find it convenient to use pseudocoding. In many cases you can describe the algorithm in the program itself.
* Include the source code of your program. Comment your program properly.
* If possible, try to find analytic solutions, or known limits in order to test your program when developing the code.
* Include your results either in figure form or in a table. Remember to label your results. All tables and figures should have relevant captions and labels on the axes.
* Try to evaluate the reliabilty and numerical stability/precision of your results. If possible, include a qualitative and/or quantitative discussion of the numerical stability, eventual loss of precision etc.
* Try to give an interpretation of you results in your answers to the problems.
* Critique: if possible include your comments and reflections about the exercise, whether you felt you learnt something, ideas for improvements and other thoughts you've made when solving the exercise. We wish to keep this course at the interactive level and your comments can help us improve it.
* Try to establish a practice where you log your work at the computerlab. You may find such a logbook very handy at later stages in your work, especially when you don't properly remember what a previous test version of your program did. Here you could also record the time spent on solving the exercise, various algorithms you may have tested or other topics which you feel worthy of mentioning.
===== Format for electronic delivery of report and programs =====
The preferred format for the report is a PDF file. You can also use DOC or postscript formats or as an ipython notebook file. As programming language we prefer that you choose between C/C++, Fortran2008 or Python. The following prescription should be followed when preparing the report:
* Use Devilry to hand in your projects, log in at URL:"http://devilry.ifi.uio.no" with your normal UiO username and password and choose either 'fysstk3155' or 'fysstk4155'. There you can load up the files within the deadline.
* Upload _only_ the report file! For the source code file(s) you have developed please provide us with your link to your github domain. The report file should include all of your discussions and a list of the codes you have developed. Do not include library files which are available at the course homepage, unless you have made specific changes to them.
* In your git repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters.
* In this and all later projects, you should include tests (for example unit tests) of your code(s).
* Comments from us on your projects, approval or not, corrections to be made etc can be found under your Devilry domain and are only visible to you and the teachers of the course.
Finally,
we encourage you to work two and two together. Optimal working groups consist of
2-3 students. You can then hand in a common report.
===== Software and needed installations =====
If you have Python installed (we recommend Python3) and you feel pretty familiar with installing different packages,
we recommend that you install the following Python packages via _pip_ as
o pip install numpy scipy matplotlib ipython scikit-learn tensorflow sympy pandas pillow
For Python3, replace _pip_ with _pip3_.
See below for a discussion of _tensorflow_ and _scikit-learn_.
For OSX users we recommend also, after having installed Xcode, to install _brew_. Brew allows
for a seamless installation of additional software via for example
o brew install python3
For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution
you can use _pip_ as well and simply install Python as
o sudo apt-get install python3 (or python for python2.7)
etc etc.
If you don't want to install various Python packages with their dependencies separately, we recommend two widely used distrubutions which set up all relevant dependencies for Python, namely
o "Anaconda":"https://docs.anaconda.com/" Anaconda is an open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system _conda_
o "Enthought canopy":"https://www.enthought.com/product/canopy/" is a Python distribution for scientific and analytic computing distribution and analysis environment, available for free and under a commercial license.
Popular software packages written in Python for ML are
* "Scikit-learn":"http://scikit-learn.org/stable/",
* "Tensorflow":"https://www.tensorflow.org/",
* "PyTorch":"http://pytorch.org/" and
* "Keras":"https://keras.io/".
These are all freely available at their respective GitHub sites. They
encompass communities of developers in the thousands or more. And the number
of code developers and contributors keeps increasing.
doc/src/Projects/2018/Project1/clean.sh
Executable
+3
View File
@@ -0,0 +1,3 @@
#!/bin/sh
doconce clean
rm -rf *.pdf *.tex ipynb*.tar.gz *.html ._*.html *~ reveal.js Trash README.txt
doc/src/Projects/2018/Project1/make.sh
Executable
+79
View File
@@ -0,0 +1,79 @@
#!/bin/sh
set -x
function system {
"$@"
if [ $? -ne 0 ]; then
echo "make.sh: unsuccessful command $@"
echo "abort!"
exit 1
fi
}
if [ $# -eq 0 ]; then
echo 'bash make.sh slides1|slides2'
exit 1
fi
name=$1
rm -f *.tar.gz
opt="--encoding=utf-8"
opt=
rm -f *.aux
# Plain HTML documents
html=${name}
system doconce format html $name --pygments_html_style=default --html_style=bloodish --html_links_in_new_window --html_output=$html $opt
system doconce split_html $html.html --method=space10
# Bootstrap style
html=${name}-bs
system doconce format html $name --html_style=bootstrap --pygments_html_style=default --html_admon=bootstrap_panel --html_output=$html $opt
system doconce split_html $html.html --method=split --pagination --nav_button=bottom
# Ordinary plain LaTeX document
system doconce format pdflatex $name --print_latex_style=trac --latex_admon=paragraph $opt
system doconce ptex2tex $name envir=print
# Add special packages
doconce subst "% Add user's preamble" "\g<1>\n\\usepackage{simplewick}" $name.tex
doconce replace 'section{' 'section*{' $name.tex
pdflatex -shell-escape $name
pdflatex -shell-escape $name
mv -f $name.pdf ${name}.pdf
cp $name.tex ${name}.tex
# Publish
dest=../../../../Projects/2018
if [ ! -d $dest/$name ]; then
mkdir $dest/$name
mkdir $dest/$name/pdf
mkdir $dest/$name/html
mkdir $dest/$name/ipynb
fi
cp ${name}*.tex $dest/$name/pdf
cp ${name}*.pdf $dest/$name/pdf
cp -r ${name}*.html ._${name}*.html $dest/$name/html
# Figures: cannot just copy link, need to physically copy the files
if [ -d fig-${name} ]; then
if [ ! -d $dest/$name/html/fig-$name ]; then
mkdir $dest/$name/html/fig-$name
fi
cp -r fig-${name}/* $dest/$name/html/fig-$name
fi
cp ${name}.ipynb $dest/$name/ipynb
ipynb_tarfile=ipynb-${name}-src.tar.gz
if [ ! -f ${ipynb_tarfile} ]; then
cat > README.txt <<EOF
This IPython notebook ${name}.ipynb does not require any additional
programs.
EOF
tar czf ${ipynb_tarfile} README.txt
fi
cp ${ipynb_tarfile} $dest/$name/ipynb
doc/src/Splines/GradientMethods.ipynb
+540
View File
File diff suppressed because one or more lines are too long
doc/web/course.do.txt
+59 -11
View File
@@ -86,15 +86,33 @@ ${text_types(ch)}
!split
===== Projects Fall 2017 =====
===== Projects Fall 2018 =====
=== Project ===
=== Project 1, Deadline October 1 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2017/Project/pdf/Project.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2017/Project/pdf/Project.pdf"
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/pdf/Project1.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/pdf/Project1.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2017/Project/html/Project.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2017/Project/html/Project-bs.html"
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/html/Project1.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/html/Project1-bs.html"
=== Project 2, Deadline November 5 ===
* LaTeX and PDF:
* "Latex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project2/pdf/Project2.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project2/pdf/Project2.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project2/html/Project2.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project2/html/Project2-bs.html"
=== Project 3, Deadline November 30 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project3/pdf/Project3.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project3/pdf/Project3.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project3/html/Project3.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project3/html/Project3-bs.html"
=== Course content ===
@@ -185,9 +203,39 @@ _General Machine Learning Books_:
!split
===== Teaching schedule Fall 2018 =====
|----------------------------------------------------------------------------------------------------------------------------|
| Week and days | Topics to be covered | Projects and deadlines | Reading assignments| Lab activities |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 34| Introduction and Regressions analysis | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 35 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 36 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 37 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 38 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 39 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 40 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 41 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 42 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 43 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 44 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 45 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 46 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 47 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 48 | | | | |
|----------------------------------------------------------------------------------------------------------------------------|
doc/web/course.html
+90 -22
View File
@@ -93,22 +93,25 @@ div { text-align: justify; text-justify: inter-word; }
'___sec10'),
('Python and Scikit Learn, a short guide', 2, None, '___sec11'),
('Teach yourself C++', 2, None, '___sec12'),
('Projects Fall 2017', 2, None, '___sec13'),
('Project', 3, None, '___sec14'),
('Course content', 3, None, '___sec15'),
('Learning outcomes', 2, None, '___sec16'),
('Prerequisites', 2, None, '___sec17'),
('The course has two central parts', 2, None, '___sec18'),
('Projects Fall 2018', 2, None, '___sec13'),
('Project 1, Deadline October 1', 3, None, '___sec14'),
('Project 2, Deadline November 5', 3, None, '___sec15'),
('Project 3, Deadline November 30', 3, None, '___sec16'),
('Course content', 3, None, '___sec17'),
('Learning outcomes', 2, None, '___sec18'),
('Prerequisites', 2, None, '___sec19'),
('The course has two central parts', 2, None, '___sec20'),
('Statistical analysis and optimization of data',
3,
None,
'___sec19'),
('Machine learning', 3, None, '___sec20'),
'___sec21'),
('Machine learning', 3, None, '___sec22'),
('"Possible '
'textbooks":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Textbooks"',
2,
None,
'___sec21')]}
'___sec23'),
('Teaching schedule Fall 2018', 2, None, '___sec24')]}
end of tocinfo -->
<body>
@@ -529,28 +532,66 @@ formulas in HTML or ipython notebook files.
<!-- !split -->
<h2 id="___sec13">Projects Fall 2017 </h2>
<h2 id="___sec13">Projects Fall 2018 </h2>
<h3 id="___sec14">Project </h3>
<h3 id="___sec14">Project 1, Deadline October 1 </h3>
<ul>
<li> LaTeX and PDF:</li>
<ul>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2017/Project/pdf/Project.tex" target="_self">LaTex file</a></li>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2017/Project/pdf/Project.pdf" target="_self">PDF file</a></li>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/pdf/Project1.tex" target="_self">LaTex file</a></li>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/pdf/Project1.pdf" target="_self">PDF file</a></li>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2017/Project/html/Project.html" target="_self">Plain html</a></li>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2017/Project/html/Project-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/html/Project1.html" target="_self">Plain html</a></li>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/html/Project1-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
</ul>
<h3 id="___sec15">Course content </h3>
<h3 id="___sec15">Project 2, Deadline November 5 </h3>
<ul>
<li> LaTeX and PDF:</li>
<ul>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project2/pdf/Project2.tex" target="_self">Latex file</a></li>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project2/pdf/Project2.pdf" target="_self">PDF file</a></li>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project2/html/Project2.html" target="_self">Plain html</a></li>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project2/html/Project2-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
</ul>
<h3 id="___sec16">Project 3, Deadline November 30 </h3>
<ul>
<li> LaTeX and PDF:</li>
<ul>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project3/pdf/Project3.tex" target="_self">LaTex file</a></li>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project3/pdf/Project3.pdf" target="_self">PDF file</a></li>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project3/html/Project3.html" target="_self">Plain html</a></li>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project3/html/Project3-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
</ul>
<h3 id="___sec17">Course content </h3>
<p>
Probability theory and statistical methods play a central role in science. Nowadays we are
@@ -569,7 +610,7 @@ tools of probability theory, the aim of this course is to expose you to central
This course covers thus topics like Monte Carlo methods and Markov chains, Bayesian statistics, error estimates, various linear methods, optimization of data and error analysis and central algorithms in machine learning.
The course has several numerical projects and numerical exercises that are meant to illustrate the theory.
<h2 id="___sec16">Learning outcomes </h2>
<h2 id="___sec18">Learning outcomes </h2>
<p>
The course introduces a variety of central algorithms and methods
@@ -586,19 +627,19 @@ essential for studies of data analysis and machine learning. The course is proje
<li> Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.</li>
</ul>
<h2 id="___sec17">Prerequisites </h2>
<h2 id="___sec19">Prerequisites </h2>
<p>
Basic knowledge in programming and numerics. Required courses are the equivalents to the University of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one of the corresponding computing and programming courses INF1000/INF1110 or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1xxx.
<h2 id="___sec18">The course has two central parts </h2>
<h2 id="___sec20">The course has two central parts </h2>
<ol>
<li> Statistical analysis and optimization of data</li>
<li> Machine learning</li>
</ol>
<h3 id="___sec19">Statistical analysis and optimization of data </h3>
<h3 id="___sec21">Statistical analysis and optimization of data </h3>
<p>
The following topics will be covered
@@ -615,7 +656,7 @@ The following topics will be covered
<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data.</li>
</ul>
<h3 id="___sec20">Machine learning </h3>
<h3 id="___sec22">Machine learning </h3>
<p>
The following topics will be covered
@@ -631,7 +672,7 @@ The following topics will be covered
All the above topics will be supported by examples, hands-on exercises and project work.
<h2 id="___sec21"><a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Textbooks" target="_self">Possible textbooks</a> </h2>
<h2 id="___sec23"><a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Textbooks" target="_self">Possible textbooks</a> </h2>
<p>
<b>General learning book on statistical analysis</b>:
@@ -651,6 +692,33 @@ All the above topics will be supported by examples, hands-on exercises and proje
<li> David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press</li>
</ul>
<!-- !split -->
<h2 id="___sec24">Teaching schedule Fall 2018 </h2>
<p>
<table border="1">
<thead>
<tr><th align="center">Week and days</th> <th align="center"> Topics to be covered </th> <th align="center">Projects and deadlines</th> <th align="center">Reading assignments</th> <th align="center">Lab activities</th> </tr>
</thead>
<tbody>
<tr><td align="center"> Week 34 </td> <td align="center"> Introduction and Regressions analysis </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 35 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 36 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 37 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 38 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 39 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 40 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 41 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 42 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 43 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 44 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 45 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 46 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 47 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 48 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
</tbody>
</table>
<!-- ------------------- end of main content --------------- -->