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

Variable associations and false discoveries


Day 2- 2-8 pm Berlin time

  • Unsupervised statistical problems:

Principal Component Analysis

  • 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