Machine Learning Overview

All machine learning models, wether they are probabilistic or non-probabilistic, parametric or non-parametric, generative or disciminative have in common that they are trained on some sort of trainig data. This seperates them from rule-based systems where the developer explicitly models his knowledge leading to specific models. This qualification line introduces the basic mathematical concepts and gives a broad overview of the most common machine learning models.

Bayesian Classification

[slides] Histograms for bayesian classification

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[notebook] KDE and KNN with Python

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[slides] Modelling of Priors

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Decision Trees and Random Forests

[slides] Decision Tree

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[slides] Random Forest

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[notebook] Decision Tree and Random Forest with Python

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Logistic Regression

[slides] Logistic Regression

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[notebook] Logistic Regression with Python using Scikit-Learn

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Support Vector Machine

[slides] Support Vector Machines

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[notebook] Support Vector Machine with Python using Scikit-Learn

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Neural Networks

[slides] Neural Networks - Basics

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Additional Topics

[slides] Ensambles and Boosting

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