Bert Gollnick

We will perform a Market Basket Analysis (also called Association Rules). This techniques shows which items occur together, like “users who bought X, also bought Y and Z”. We will use this method to find out, which properties are important to conclude that a mushroom is edible.

Introduction In this tutorial we will take a look at measurements of Hubble (the telescope). Besides taking beautiful pictures, it measured speed and distance of Super-Novae. Similar data was used in 1929 by Hubble (the person) and he found out that there is a linear relationship. We will create a linear model based on observations …

Training a simple linear model Read More »

H2O is an open source platform for machine learning. It can be installed locally. It offers API access to R. This lecture will give you an introduction on how to use it. Objectives: Installation and Usage of H2O Requirements: R Data-Mining

In this tutorial we will take a look at deep learning with neuralnet package – a package with which you can create neural networks. Objectives: deep learning with neuralnet package; create a model for mushroom classification Requirements: R Basics

In this tutorial you will learn what the bias-variance tradeoff is and how this problem can be solved with cross validation.

We will create a Random Forest and a Support Vector Machine model for income prediction. We will also learn what a Receiver Operating Characteristic (ROC) is and how to interpret it. Objectives: Model Creation, Random Forest, SVM, Prediction, ROC-Curve Requirements: R Basics, R Data Mining

broom is a helpful package for transforming models into dataframes for further analysis. modelr has some useful functions for working with models. Objectives: learn to handle packages broom and modelr, randomForest model, linear, quadratic and cubic model, and a bit map() from purrr package Requirement: piping, experience of dplyr might help

Exploratory factor analysis is performed to find a lower number of unobserved variables (factors) in a larger number of correlated variables. It is a statistical technique for dimension reduction. We will analyse “forest fires” dataset.