Day 1 - 2-8 pm Berlin time

• General Introduction

Data mining, -omics and machine learning
Experimental Design

• Jupyter for R  

Introduction to advanced R data libraries
Introduction to R frameworks for machine learning

• Multivariate data

Generalities
Variable associations and false discoveries

Day 2- 2-8 pm Berlin time

• Unsupervised statistical problems:

Principal Component Analysis
Clustering

• PCA as a data model, introduction to validation

• Supervised learning: regression and classification

Day 3 - 2-8 pm Berlin time

• model tuning and validation

• resampling techniques

• lasso-penalised linear and logistic regression

Day 4- 2-8 pm Berlin time

• Random Forest for regression and classification

• Weak learning: the boosting approach

Day 5- 2-8 pm Berlin time

• Advanced model and data visualization

• Model and variable selection: the machine learning paradigm

• Kahoot quiz: let’s test our machine learning skills!

Datasets: freely available ‘omics datasets