mhjensen
257 lines
13 KiB
HTML
257 lines
13 KiB
HTML
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<meta name="description" content="Week 34: Introduction to the course, Logistics and Practicalities">
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'sections': [('Overview of first week', 2, None, '___sec0'),
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<a class="navbar-brand" href="week34-bs.html">Week 34: Introduction to the course, Logistics and Practicalities</a>
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<ul class="nav navbar-nav navbar-right">
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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="._week34-bs001.html#___sec0" style="font-size: 80%;"><b>Overview of first week</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs002.html#___sec1" style="font-size: 80%;"><b>Thursday August 20</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs003.html#___sec2" style="font-size: 80%;"><b>Lectures and ComputerLab</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs004.html#___sec3" style="font-size: 80%;"><b>Course Format</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs005.html#___sec4" style="font-size: 80%;"><b>Teachers</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs006.html#___sec5" style="font-size: 80%;"><b>Deadlines for projects (tentative)</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs007.html#___sec6" style="font-size: 80%;"><b>Recommended textbooks</b></a></li>
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<!-- navigation toc: --> <li><a href="#___sec7" style="font-size: 80%;"><b>Prerequisites</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs009.html#___sec8" style="font-size: 80%;"><b>Learning outcomes</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs010.html#___sec9" style="font-size: 80%;"><b>Topics covered in this course: Statistical analysis and optimization of data</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs011.html#___sec10" style="font-size: 80%;"><b>Topics covered in this course: Machine Learning</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs012.html#___sec11" style="font-size: 80%;"><b>Extremely useful tools, strongly recommended</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs013.html#___sec12" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs014.html#___sec13" style="font-size: 80%;"><b>Introduction</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs015.html#___sec14" style="font-size: 80%;"><b>What is Machine Learning?</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs016.html#___sec15" style="font-size: 80%;"><b>Types of Machine Learning</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs017.html#___sec16" style="font-size: 80%;"><b>Software and needed installations</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs018.html#___sec17" style="font-size: 80%;"><b>Python installers</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs019.html#___sec18" style="font-size: 80%;"><b>Useful Python libraries</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs020.html#___sec19" style="font-size: 80%;"><b>Installing R, C++, cython or Julia</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs021.html#___sec20" style="font-size: 80%;"><b>Installing R, C++, cython, Numba etc</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs022.html#___sec21" style="font-size: 80%;"><b>Numpy examples and Important Matrix and vector handling packages</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs023.html#___sec22" style="font-size: 80%;"><b>Basic Matrix Features</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs024.html#___sec23" style="font-size: 80%;"> Some famous Matrices</a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs025.html#___sec24" style="font-size: 80%;"> More Basic Matrix Features</a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs026.html#___sec25" style="font-size: 80%;"><b>Numpy and arrays</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs027.html#___sec26" style="font-size: 80%;"><b>Matrices in Python</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs028.html#___sec27" style="font-size: 80%;"><b>Meet the Pandas</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs029.html#___sec28" style="font-size: 80%;"><b>Friday August 21</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs030.html#___sec29" style="font-size: 80%;"><b>Reading Data and fitting</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs031.html#___sec30" style="font-size: 80%;"><b>Friday August 21</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec31" style="font-size: 80%;"> Simple linear regression model using <b>scikit-learn</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec32" style="font-size: 80%;"> To our real data: nuclear binding energies. Brief reminder on masses and binding energies</a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec33" style="font-size: 80%;"> Organizing our data</a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec34" style="font-size: 80%;"> Seeing the wood for the trees</a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec35" style="font-size: 80%;"> And what about using neural networks?</a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec36" style="font-size: 80%;"><b>A first summary</b></a></li>
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</ul>
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</li>
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<a name="part0008"></a>
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<!-- !split -->
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<h2 id="___sec7" class="anchor">Prerequisites </h2>
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<p>
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Basic knowledge in programming and mathematics, with an emphasis on
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linear algebra. Knowledge of Python or/and C++ as programming
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languages is strongly recommended and experience with Jupiter notebook
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is recommended. Required courses are the equivalents to the University
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of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one
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of the corresponding computing and programming courses INF1000/INF1110
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or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities
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offer nowadays a basic programming course (often compulsory) where
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Python is the recurring programming language.
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<p>
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<li><a href="._week34-bs000.html">1</a></li>
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<li><a href="._week34-bs001.html">2</a></li>
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<li><a href="._week34-bs002.html">3</a></li>
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<li><a href="._week34-bs003.html">4</a></li>
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<li><a href="._week34-bs004.html">5</a></li>
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<li><a href="._week34-bs005.html">6</a></li>
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<li><a href="._week34-bs006.html">7</a></li>
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<li><a href="._week34-bs007.html">8</a></li>
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<li class="active"><a href="._week34-bs008.html">9</a></li>
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<li><a href="._week34-bs009.html">10</a></li>
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<li><a href="._week34-bs010.html">11</a></li>
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<li><a href="._week34-bs011.html">12</a></li>
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<li><a href="._week34-bs012.html">13</a></li>
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<li><a href="._week34-bs013.html">14</a></li>
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<li><a href="._week34-bs014.html">15</a></li>
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<li><a href="._week34-bs015.html">16</a></li>
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<li><a href="._week34-bs016.html">17</a></li>
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<li><a href="._week34-bs017.html">18</a></li>
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<li><a href="">...</a></li>
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<li><a href="._week34-bs032.html">33</a></li>
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