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Teaching
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Teaching
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# Teaching Material for Numerical Methods and Machine Learning Created by Philipp Eller (philipp.eller@tum.de) Contents: File | Content --- | --- [optimization.ipynb](https://github.com/philippeller/Teaching/blob/master/optimization.ipynb) | Basics of optimization algorithms, illustrated using the 2d Rosenbrock test function [decorrelation_and_pca.ipynb](https://github.com/philippeller/Teaching/blob/master/decorrelation_and_pca.ipynb) | De-correlation of datasets and dimensionality reduction via principle component analysis (PCA) [clustering_basics.ipynb](https://github.com/philippeller/Teaching/blob/master/clustering_basics.ipynb) | Basics of clustering algorithms: k-Means and Gaussian mixture model (GMM) [clustering_examples.ipynb](https://github.com/philippeller/Teaching/blob/master/clustering_examples.ipynb) | Some more fun applications of clsutering [expectation_maximization_1d.ipynb](https://github.com/philippeller/Teaching/blob/master/expectation_maximization_1d.ipynb) | Extra norebook illustrating the EM algorithm in 1d [my_mystery_module.py](https://github.com/philippeller/Teaching/blob/master/my_mystery_module.py) | Some code used in the clustering notebooks above [classification.ipynb](https://github.com/philippeller/Teaching/blob/master/classification.ipynb) | Classification using various algorithms applied to the MNIST dataset [regression.ipynb](https://github.com/philippeller/Teaching/blob/master/regression.ipynb) | Regression using various algorithms applied to the Boston housing dataset [deep_learning.ipynb](https://github.com/philippeller/Teaching/blob/master/deep_learning.ipynb) | Various Deep Learning Models applied to the MNIST dataset [variational_autoencoder.ipynb](https://github.com/philippeller/Teaching/blob/master/variational_autoencoder.ipynb) | Variational auto encoder and generator [Exoplanet.ipynb](https://github.com/philippeller/Teaching/blob/master/Exoplanet.ipynb) | Data Analysis example for an Exoplanet Analysis
