CUrriculum

Monday - Classes from 9:30 to 17:30

  •       General Introduction

○    Data mining, -omics and machine learning
○    Experimental Design

  •     Jupyter for R 

○    Introduction
○    Data structures
○    Reading and Writing data
○    Data visualization


Tuesday - Classes from 9:30 to 17:30

  •   Multivariate data

○       Generalities
○       Variable associations and false discoveries

  • Unsupervised visualization:

○       Principal Component Analysis
○       Clustering

  • PCA  as a data model, introduction to validation


 
Wednesday - Classes from 9:30 to 17:30

  • Supervised learning: regression and classification

○       model tuning and validation
○       resampling techniques
○       lasso-penalised linear and logistic regression


Thursday - Classes from 9:30 to 17:30

  •      Random Forest for regression and classification