Day 1 - 2-8 pm Berlin time
Data mining, -omics and machine learning
Experimental Design
Introduction to advanced R data libraries
Introduction to R frameworks for machine learning
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
- 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