Abstract

This is an Introduction to Machine Learning in R, in which you'll learn the basics of unsupervised learning for pattern recognition and supervised learning for prediction. At the end of this workshop, we hope that you will:

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

• What are the main categories in Machine Learning algorithms?
• How can I perform exploratory data analysis?
• What are the main part of a clustering process?
• How can a create a decision tree?
• How can I assess a linear regression model?

Learning Objectives

• Understand the ML taxonomy and the commonly used machine learning algorithms for analysing -omics data
• Understand differences between ML algorithms categories and to which kind of problem they can be applied
• Understand different applications of ML in different -omics studies
• Use some basic, widely used R packages for ML
• Interpret and visualize the results obtained from ML analyses on omics datasets
• Apply the ML techniques to analyse their own datasets