mhjensen
183 lines
8.4 KiB
HTML
183 lines
8.4 KiB
HTML
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<meta name="description" content="Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities">
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<title>Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities</title>
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{'highest level': 2,
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'sections': [('Overview of first week', 2, None, '___sec0'),
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('Lectures and ComputerLab', 2, None, '___sec1'),
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('Course Format', 2, None, '___sec2'),
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('Topics covered in this course: Statistical analysis and '
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'optimization of data',
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('Topics covered in this course: Machine Learning',
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('Extremely useful tools, strongly recommended',
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<a class="navbar-brand" href="Intro2Course-bs.html">Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities</a>
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</div>
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<div class="navbar-collapse collapse navbar-responsive-collapse">
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<ul class="nav navbar-nav navbar-right">
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<li class="dropdown">
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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="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
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<!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
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</li>
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0007"></a>
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<!-- !split -->
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<h2 id="___sec6" class="anchor">Learning outcomes </h2>
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<p>
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<div class="panel panel-default">
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<div class="panel-body">
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<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
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<p>
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This course aims at giving you insights and knowledge about many of the central algorithms used in Data Analysis and Machine Learning. The course is project based and through various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach, although we will try to link it with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
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<ul>
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<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;</li>
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<li> Be capable of extending the acquired knowledge to other systems and cases;</li>
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<li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
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<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;</li>
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<li> Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;</li>
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<li> Learn about about decision trees, random forests, bagging and boosting methods;</li>
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<li> Learn about support vector machines and kernel transformations;</li>
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<li> Reduction of data sets, from PCA to clustering;</li>
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<li> Autoencoders and Reinforcement Learning;</li>
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<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).</li>
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</ul>
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</div>
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</div>
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<p>
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<p>
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<!-- navigation buttons at the bottom of the page -->
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<ul class="pagination">
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<li><a href="._Intro2Course-bs006.html">«</a></li>
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<li><a href="._Intro2Course-bs000.html">1</a></li>
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<li><a href="._Intro2Course-bs001.html">2</a></li>
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<li><a href="._Intro2Course-bs002.html">3</a></li>
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<li><a href="._Intro2Course-bs003.html">4</a></li>
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<li><a href="._Intro2Course-bs004.html">5</a></li>
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<li><a href="._Intro2Course-bs005.html">6</a></li>
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<li><a href="._Intro2Course-bs006.html">7</a></li>
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<li class="active"><a href="._Intro2Course-bs007.html">8</a></li>
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<li><a href="._Intro2Course-bs008.html">9</a></li>
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<li><a href="._Intro2Course-bs009.html">10</a></li>
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<li><a href="._Intro2Course-bs010.html">11</a></li>
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<li><a href="._Intro2Course-bs011.html">12</a></li>
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<li><a href="._Intro2Course-bs008.html">»</a></li>
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