Professor: Massimiliano Bordoni

Programme
The course aims to provide PhD students with the basic principles and functions of the R programming language necessary for the development and implementation of statistical and probabilistic models—either data-driven or based on machine learning—for problem-solving and the estimation of variables and parameters in the field of Earth and Environmental Sciences. The concepts covered in the course will be applied to case studies and examples drawn from various areas within Earth and Environmental Sciences. The proposed course will consist of several lessons, following the program below:

-Introduction to the R language: basic principles and commands for developing data-driven and machine learning models
-Simple and multivariate regression models: how to relate predictor variables to target parameters in a model
-Data-driven and machine learning models for estimating the spatial distribution of variables
-Data-driven and machine learning models for estimating time series and forecasting variables over time
-Practical session

Language: English