mhjensen
230 lines
11 KiB
HTML
230 lines
11 KiB
HTML
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<title>Data Analysis and Machine Learning: Introduction and Representing data</title>
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'sections': [('What is Machine Learning?', 2, None, '___sec0'),
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('Types of Machine Learning', 2, None, '___sec1'),
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('Different algorithms', 2, None, '___sec2'),
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('Software and needed installations', 2, None, '___sec3'),
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('Python installers', 2, None, '___sec4'),
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('Installing R, C++, cython or Julia', 2, None, '___sec5'),
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('Installing R, C++, cython or Julia', 2, None, '___sec6'),
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('Introduction to Jupyter notebook and available tools',
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2,
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None,
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('Representing data, more examples', 2, None, '___sec8'),
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('Predator-Prey model from ecology', 2, None, '___sec9'),
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('Case study from Hudson bay', 2, None, '___sec10'),
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('Hudson bay data', 2, None, '___sec11'),
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('Plotting the data', 2, None, '___sec12'),
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('Hares and lynx in Hudson bay from 1900 to 1920',
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2,
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None,
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('Why now create a computer model for the hare and lynx '
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('The traditional (top-down) approach', 2, None, '___sec15'),
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('Basic mathematics notation', 2, None, '___sec16'),
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('Basic dynamics of the population of hares',
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('Basic dynamics of the population of lynx', 2, None, '___sec18'),
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('Evolution equations', 2, None, '___sec19'),
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<a class="navbar-brand" href="How2ReadData-bs.html">Data Analysis and Machine Learning: Introduction and Representing data</a>
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
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<ul class="dropdown-menu">
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs001.html#___sec0" style="font-size: 80%;">What is Machine Learning?</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs002.html#___sec1" style="font-size: 80%;">Types of Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs003.html#___sec2" style="font-size: 80%;">Different algorithms</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs004.html#___sec3" style="font-size: 80%;">Software and needed installations</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs005.html#___sec4" style="font-size: 80%;">Python installers</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs006.html#___sec5" style="font-size: 80%;">Installing R, C++, cython or Julia</a></li>
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<!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;">Installing R, C++, cython or Julia</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs008.html#___sec7" style="font-size: 80%;">Introduction to Jupyter notebook and available tools</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs009.html#___sec8" style="font-size: 80%;">Representing data, more examples</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs010.html#___sec9" style="font-size: 80%;">Predator-Prey model from ecology</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs011.html#___sec10" style="font-size: 80%;">Case study from Hudson bay</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs012.html#___sec11" style="font-size: 80%;">Hudson bay data</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs013.html#___sec12" style="font-size: 80%;">Plotting the data</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs014.html#___sec13" style="font-size: 80%;">Hares and lynx in Hudson bay from 1900 to 1920</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs015.html#___sec14" style="font-size: 80%;">Why now create a computer model for the hare and lynx populations?</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs016.html#___sec15" style="font-size: 80%;">The traditional (top-down) approach</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs017.html#___sec16" style="font-size: 80%;">Basic mathematics notation</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs018.html#___sec17" style="font-size: 80%;">Basic dynamics of the population of hares</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs019.html#___sec18" style="font-size: 80%;">Basic dynamics of the population of lynx</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs020.html#___sec19" style="font-size: 80%;">Evolution equations</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs021.html#___sec20" style="font-size: 80%;">Adapt the model to the Hudson Bay case</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs022.html#___sec21" style="font-size: 80%;">The program</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs023.html#___sec22" style="font-size: 80%;">The plot</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs024.html#___sec23" style="font-size: 80%;">Linear regression in Python</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs025.html#___sec24" style="font-size: 80%;">Linear Least squares in R</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs026.html#___sec25" style="font-size: 80%;">Non-Linear Least squares in R</a></li>
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</ul>
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</li>
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<a name="part0007"></a>
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<!-- !split -->
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<h2 id="___sec6" class="anchor">Installing R, C++, cython or Julia </h2>
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<p>
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For the C++ affecianodas, Jupyter/IPython notebook allows you also to install C++ and run codes written in this language
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interactively in the browser. Since we will emphasize writing many of the algorithms yourself, you can thus opt for
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either Python or C++ as programming languages.
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<p>
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To add more entropy, <b>cython</b> can also be used when running your notebooks. It means that Python with the Jupyter/IPython notebook
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setup allows you to integrate widely popular softwares and tools for scientific computing. With its versatility,
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including symbolic operations, Python offers a unique computational environment. Your Jupyter/IPython notebook
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can easily be converted into a nicely rendered <b>PDF</b> file or a Latex file for further processing. For example, convert to latex as
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<p>
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If you use the light mark-up language <b>doconce</b> you can convert a standard ascii text file into various HTML
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formats, ipython notebooks, latex files, pdf files etc.
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<li><a href="">...</a></li>
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