Lectures schedule:

19 January 2026 9 am – 1 pm: lecture 1
20 January 2026 9 am – 1 pm: lecture 2
21 January 2026 9 am – 1 pm: lecture 3
22 January 2026 9 am – 1 pm: lecture 4
23 January 2026 9 am – 1 pm: lecture 5
26 January 2026 9 am – 1 pm: exam

The lectures will be held in Room D7 (level D), Department of Earth and Environmental Sciences, Via Ferrata 1.

Bring a laptop PC in all the lectures.

Details will be provided to registered PhD candidates ahead of the start of the course.

Course description

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 and final exam