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Statistical Learning Theory
These are exam preparation notes, subpar in quality and certainly not of divine quality. See the index with all articles in this series In a classification problem the desired goal is to reduce the generalization error \(E^G\). Unfortunately during training it is only possible to evaluate the classifier against a limited amount of data  the test data set. Therefore we can only measure \(E^T\). The problem we want to...
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34C3 Economics of Climate Change Lightning Talk
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Docker Images with Gitlab CI
You want to have docker tags that match your git branches? Here is how to do it with Gitlab CI. A lot of my projects have a CI pipeline that builds a docker image. Of course I do not want to always deploy the :latest tag, because that makes reproducibility and rollbacks hard. I always push to :latest. Also I want to reference by: tags/branches commit hash For this repo...
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34C3 Day 1
Day one of the 34C3 is over. The new location in Leipzig is a lot more spacey and loftey, but I liked the old location in the CCH more. Somehow I felt that there were fewer eastereggs and hidden nuggets than in previous congresses. I guess everyone still needs to adapt to the new environment. Hopefully in the coming days there will be more. Tomorrow will also be my first...
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Estimation theory  Kernel Density Estimation
Chapters General Terms and tools PCA PCA Hebbian Learning KernelPCA Source Separation ICA Infomax ICA Second Order Source Separation FastICA Stochastic Optimization Clustering kmeans Clustering Pairwise Clustering SelfOrganising Maps Locally Linear Embedding Estimation Theory Density Estimation Kernel Density Estimation Parametric Density Estimation Mixture Models  Estimation Models Density Estimation The goal of density estimation is to be able to give a density estimation for each coordinate in the vector space....
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Stochastic Optimization
Chapters General Terms and tools PCA PCA Hebbian Learning KernelPCA Source Separation ICA Infomax ICA Second Order Source Separation FastICA Stochastic Optimization Clustering kmeans Clustering Pairwise Clustering SelfOrganising Maps Locally Linear Embedding Estimation Theory Density Estimation Kernel Density Estimation Parametric Density Estimation Mixture Models  Estimation Models Simulated Annealing Simulated annealing is oriented in crystallization procedures in nature where the lowest energy state is achieved only when the temperature is...
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Clustering  kmeans & SOM
Chapters General Terms and tools PCA PCA Hebbian Learning KernelPCA Source Separation ICA Infomax ICA Second Order Source Separation FastICA Stochastic Optimization Clustering kmeans Clustering Pairwise Clustering SelfOrganising Maps Locally Linear Embedding Estimation Theory Density Estimation Kernel Density Estimation Parametric Density Estimation Mixture Models  Estimation Models Kmeans Clustering Kmeans Clustering is good at finding equally sized clusters of data points. Parameters Distance Function (Usually Euclidean) Number of clusters Drawbacks...
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Source Separation (ICA)
Chapters General Terms and tools PCA PCA Hebbian Learning KernelPCA Source Separation ICA Infomax ICA Second Order Source Separation FastICA Stochastic Optimization Clustering kmeans Clustering Pairwise Clustering SelfOrganising Maps Locally Linear Embedding Estimation Theory Density Estimation Kernel Density Estimation Parametric Density Estimation Mixture Models  Estimation Models Independent Component Analysis (ICA) ICA allows the reconstruction of mixed signals. This could for example be multiple speakers on one audio track. Requirements...
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