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Morten Hjorth-Jensen
2024-08-18 22:06:49 +02:00
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@@ -268,123 +268,6 @@ const thebe_selector_output = ".output, .cell_output"
Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
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<a class="reference internal" href="exercisesweek36.html">
Exercises week 36
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<a class="reference internal" href="week36.html">
Week 36: Statistical interpretation of Linear Regression and Resampling techniques
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<a class="reference internal" href="exercisesweek37.html">
Exercises week 37
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<a class="reference internal" href="week37.html">
Week 37: Statistical interpretations and Resampling Methods
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<a class="reference internal" href="exercisesweek38.html">
Exercises week 38
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<a class="reference internal" href="week38.html">
Week 38: Logistic Regression and Optimization
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<a class="reference internal" href="exercisesweek39.html">
Exercises week 39
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<a class="reference internal" href="week39.html">
Week 39: Optimization and Gradient Methods
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<a class="reference internal" href="week40.html">
Week 40: Gradient descent methods (continued) and start Neural networks
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<a class="reference internal" href="exercisesweek41.html">
Exercises week 41
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Week 41 Neural networks and constructing a neural network code
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<a class="reference internal" href="exercisesweek42.html">
Exercises week 42
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<a class="reference internal" href="week42.html">
Week 42 Constructing a Neural Network code with introduction to Tensor flow
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<li class="toctree-l1">
<a class="reference internal" href="exercisesweek43.html">
Exercises weeks 43 and 44
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<a class="reference internal" href="week43.html">
Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
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<a class="reference internal" href="week44.html">
Week 44, Convolutional Neural Networks (CNN)
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<a class="reference internal" href="week45.html">
Week 45, Recurrent Neural Networks
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<a class="reference internal" href="week46.html">
Week 46: Decision Trees, Ensemble methods and Random Forests
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<a class="reference internal" href="week47.html">
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
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<a class="reference internal" href="exercisesweek47.html">
Exercise week 47
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<p aria-level="2" class="caption" role="heading">
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Projects
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</p>
<ul class="nav bd-sidenav">
<li class="toctree-l1">
<a class="reference internal" href="project1.html">
Project 1 on Machine Learning, deadline October 9 (midnight), 2023
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<li class="toctree-l1">
<a class="reference internal" href="project2.html">
Project 2 on Machine Learning, deadline November 17 (Midnight)
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</ul>
</div>
@@ -530,7 +413,7 @@ doconce format html exercisesweek34.do.txt -->
<!-- dom:TITLE: Exercises week 34 --><div class="tex2jax_ignore mathjax_ignore section" id="exercises-week-34">
<h1>Exercises week 34<a class="headerlink" href="#exercises-week-34" title="Permalink to this headline"></a></h1>
<p><strong>FYS-STK3155/4155</strong></p>
<p>Date: <strong>August 21-25, 2023</strong></p>
<p>Date: <strong>August 19-23, 2024</strong></p>
<div class="section" id="exercises">
<h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this headline"></a></h2>
<p>Here are three possible exercises for week 34</p>
@@ -605,7 +488,7 @@ The following simple Python instructions define our <span class="math notranslat
<div class="cell_output docutils container">
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
<span class="ne">NameError</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
<span class="nn">Input In [1],</span> in <span class="ni">&lt;cell line: 1&gt;</span><span class="nt">()</span>
<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">2</span> <span class="n">y</span> <span class="o">=</span> <span class="mf">2.0</span><span class="o">+</span><span class="mi">5</span><span class="o">*</span><span class="n">x</span><span class="o">*</span><span class="n">x</span><span class="o">+</span><span class="mf">0.1</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>