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Morten Hjorth-Jensen
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About the course
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Review of Statistics with Resampling Techniques and Linear Algebra
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1. Elements of Probability Theory and Statistical Data Analysis
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From Regression to Support Vector Machines
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3. Linear Regression
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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Exercises week 34
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Exercises week 35
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Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
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Exercises week 36
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Week 36: Statistical interpretation of Linear Regression and Resampling techniques
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Exercises week 37
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Week 37: Statistical interpretations and Resampling Methods
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Exercises week 38
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Week 38: Logistic Regression and Optimization
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Exercises week 39
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Week 39: Optimization and Gradient Methods
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Week 40: Gradient descent methods (continued) and start Neural networks
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Project 1 on Machine Learning, deadline October 9 (midnight), 2023
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<h1>Exercises week 38<a class="headerlink" href="#exercises-week-38" title="Permalink to this headline"></a></h1>
<p><strong>September 18-22, 2023</strong></p>
<p>Date: <strong>Deadline is Sunday September 24 at midnight</strong></p>
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<h2>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Permalink to this heading">#</a></h2>
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<h2>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Permalink to this headline"></a></h2>
<p>The aim of the exercises this week is to derive the equations for the bias-variance tradeoff to be used in project 1 as well as testing this for a simpler function using the bootstrap method. The exercises here can be reused in project 1 as well.</p>
<p>Consider a
dataset <span class="math notranslate nohighlight">\(\mathcal{L}\)</span> consisting of the data
@@ -467,9 +489,9 @@ studying the MSE value as function of the complexity of your model. Use ordinary
of your model complexity (the degree of the polynomial) and the number
of data points, and possibly also your training and test data using the <strong>bootstrap</strong> resampling method.
You can follow the code example in the jupyter-book at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff">https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff</a>.</p>
<p>See also the whiteboard notes from week 37 at <a class="github reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep14.pdf">CompPhysics/MachineLearning</a></p>
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<p>See also the whiteboard notes from week 37 at <a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep14.pdf">https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep14.pdf</a></p>
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