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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta name="description" content="">
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<title>Foundations of Data Mining</title>
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<a class="navbar-brand" href="#">Foundations of Data Mining</a>
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<a class="nav-link" href="https://github.com/joaquinvanschoren/ML-course">GitHub</a>
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<a class="nav-link" href="https://canvas.tue.nl/courses/5514/assignments/syllabus">Canvas</a>
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<div class="container" style="height:200vh">
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<h2 class="display-4">Tutorials</h2>
<ul>
<li><a href="00 - Prerequisites.slides.html">Prerequisites</a></li>
<li><a href="00 - Tutorial 1a - Linear Algebra.pdf">Linear Algebra (PDF)</a></li>
<li><a href="00 - Tutorial 1b - Python.slides.html">Introduction to Python</a></li>
<li><a href="00 - Tutorial 2a - Python for Data Analysis.slides.html">Python for Data Analysis</a></li>
<li><a href="00 - Tutorial 2b - OpenML.slides.html">OpenML (updated!)</a></li>
<li><a href="00 - Tutorial 3 - Feature Engineering.slides.html">Feature Engineering (updated!)</a></li>
<li><a href="00 - Tutorial 4 - Machine learning pipelines and systems.slides.html">Machine Learning Pipelines and Systems (updated!)</a></li>
<li><a href="Tutorial 11_MLP_classifier.ipynb">Multi-Layer Perceptrons [Notebook]</a></li>
<li><a href="Tutorial 12_ConvNet_classifier.ipynb">Convolutional Neural Nets [Notebook]</a></li>
<li><a href="Tutorial 13_RNN_addition.ipynb">Recurrent Neural Nets [Notebook]</a></li>
</ul>
<h2 class="display-4">Lectures</h2>
<ul>
<li><a href="01 - Course overview.slides.html">Course overview</a></li>
<li><a href="01 - Introduction.slides.html">Introduction to Machine Learning</a></li>
<li><a href="02 - Linear Models.slides.html">Linear Models</a></li>
<li><a href="03 - Model Selection.slides.html">Model Selection</a></li>
<li><a href="04 - Trees and Ensembles.slides.html">Trees and Ensembles</a></li>
<li><a href="05 - Support Vector Machines.slides.html">Support Vector Machines and kernelization</a></li>
<li><a href="06 - Bayesian Learning.slides.html">Bayesian Learning</a></li>
<li><a href="07 - pca.pdf">Principle Component Analysis [PDF]</a></li>
<li><a href="08 - random_projections.pdf">Random Projections [PDF]</a></li>
<li><a href="09-lsh.pdf">Locality Sensitive Hashing [PDF]</a></li>
<li><a href="10-clusterin.pdf">Clustering [PDF]</a></li>
<li><a href="11 - nn_primer">Neural Networks [PDF]</a></li>
<li><a href="12 - cnn.pdf">Convolutional Neural Networks [PDF]</a></li>
<li><a href="13 - sequences.pdf">Recurrent Neural Networks [PDF]</a></li>
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<p class="m-0 text-center text-white small">Joaquin Vanschoren, Anne Driemel, Vlado Menkovski, Spring 2018</p>
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