# 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**

[notebook] **KDE and KNN with Python**

[slides] **Modelling of Priors**

## Decision Trees and Random Forests

[slides] **Decision Tree**

[slides] **Random Forest**

[notebook] **Decision Tree and Random Forest with Python**

## Logistic Regression

[slides] **Logistic Regression**

[notebook] **Logistic Regression with Python using Scikit-Learn**

## Support Vector Machine

[slides] **Support Vector Machines**

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

## Neural Networks

[slides] **Neural Networks - Basics**

## Additional Topics

[slides] **Ensambles and Boosting**