Agenda

23 Jun 2025 09:00

Summer School: Introduction to Modern Generalised Additive Models in R

Università Ca’ Foscari Venezia, Campus Scientifico, Via Torino 155, Venezia Mestre

Ca’ Foscari University of Venice is pleased to present the Summer School "Introduction to Modern Generalised Additive Models in R", an intensive two-day course (June 23-24, 2025) dedicated to Generalised Additive Models (GAMs). This event is part of the advanced training program promoted by the Department of Environmental Sciences, Informatics and Statistics (DAIS) and the MIUR Department of Excellence, in collaboration with DESC.

Course Objectives
The course provides a comprehensive introduction to Generalised Additive Models (GAMs), an extension of traditional regression models that balances flexibility and interpretability for predictive and inferential analysis. The teaching approach includes theoretical lectures and hands-on practical sessions using R and the mgcv package, developed by the course instructor, Prof. Simon Wood (University of Edinburgh).

Learning Outcomes
By the end of the Summer School, participants will be able to:

  • Understand the theoretical principles behind GAMs
  • Apply the mgcv package to build and analyse GAM models
  • Evaluate and compare alternative models
  • Justify their statistical modelling choices

Target Audience
The course is designed for researchers, professionals, and students with a basic statistical background and familiarity with the R software. Prior knowledge of Generalised Linear Models (GLMs) is recommended, even at an intuitive level, but no prior experience with the mgcv package is required.

Registration Information
Participation in the Summer School requires registration. Seats are limited. For further details on registration procedures, fees, and deadlines, please visit the official event website or contact the organisers at dais.eccellente@unive.it.

Register now and take part in a unique learning opportunity!

Language

The event will be held in English

Organized by

Dipartimento di Scienze Ambientali, Informatica e Statistica [DESC-DAIS]

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