1. Machine Learning with Functional Data: From Statistical Methods to Deep Architectures
Lecturer: Alberto Suárez González (Universidad Autónoma de Madrid)
When: July 13th, from 14:00 to 19:00
Description:
This tutorial provides a comprehensive overview of machine learning methods for functional data. Participants will learn how to represent, preprocess, and analyze functional data for clustering, regression, and classification. The session also introduces deep learning architectures tailored for functional inputs. Hands-on exercises using the Python library scikit-fda will allow participants to explore these techniques with real-world examples.
Topics Covered:
Introduction to functional data and their representations
Preprocessing: smoothing, centering, scaling, dimensionality reduction
Statistical and machine learning methods: clustering, regression, classification
Deep learning architectures for functional inputs
References:
Ramos-Carreño, C., et al. (2024). scikit-fda: A Python Package for Functional Data Analysis. Journal of Statistical Software, 109(2).
Ramsay, J. O., & Silverman, B. W. (2005). Functional Data Analysis, 2nd ed. Springer.
Ferraty, F., & Vieu, P. (2006). Nonparametric Functional Data Analysis. Springer.
Hsing, T., & Eubank, R. (2015). Theoretical Foundations of Functional Data Analysis. Wiley.
2. Hidden but Powerful: Practical Insights into Latent Markov Models to Discover Dynamics in Longitudinal Data
Lecturer: Fulvia Pennoni (University of Milano-Bicocca)
When: July 13th, 9:00 to 13:00
Description:
This tutorial introduces Markov and hidden Markov models for the analysis of longitudinal data. Participants will gain both theoretical understanding and practical skills for applying these models using the LMest package in the R environment. Hands-on exercises with R and RMarkdown will enable participants to implement and interpret these models in real-world applicative cases.
Topics Covered:
Introduction to discrete latent variables
Basic and advanced Markov and hidden Markov model formulation
Estimation issues and model selection
Applications with covariates and missing data
References:
Bartolucci F., Farcomeni A., Pennoni F. (2013). Latent Markov Models for Longitudinal Data, Chapman and Hall/CRC, Boca Raton.
Pennoni, F., Pandolfi, S., Bartolucci, F. (2025). LMest: An R Package for Estimating Generalized Latent Markov Models. The R Journal, 16, 74 -101.