====== Advanced Statistical Learning (0106A) A.Y. 2026/27, Second Semester ======

Page under construction.

=====Instructors=====

  * **Francesca Chiaromonte**
    * Scuola Superiore S. Anna
    * [[https://www.santannapisa.it/it/francesca-chiaromonte]]
    * [[francesca.chiaromonte@santannapisa.it]]   
    * Office hours: TBD

  * **Salvatore Ruggieri**
    * Università di Pisa
    * [[http://pages.di.unipi.it/ruggieri/]]
    * [[salvatore.ruggieri@unipi.it]]   
    * Office hours: TBD

=====Objectives=====

The course covers advanced statistical methods for machine learning. **Module 1** introduces advanced statistical foundations for machine learning, focusing on robustness, decision-making under uncertainty, calibration, Bayesian and causal inference, and modern representation techniques. **Module 2** addresses high dimensional and large-scale data challenges, emphasizing feature selection, dimension reduction, and strategies for managing massive or imbalanced datasets. Theoretical concepts are complemented by practical exercises and project work using the R or Python programming languages. 

=====Syllabus =====

**Module 1 (INF/01, 3 ECTS)**\\
– Noise and robust statistics\\
– Statistical decision theory\\
– Classifier calibration, conformal methods and learning to reject/defer\\
– Bayesian inference\\
– Causal inference: structured causal model, potential outcome model\\
– PCA and embeddings in LLM. \\
**Module 2 (SECS-S/01, 3 ECTS)**\\
– Feature selection and regularization techniques for high-dimensional Linear and Generalized Linear Models\\
– Feature screening algorithms for ultra-high dimensional supervised problems\\
– Supervised dimension reduction; Sufficient Dimension Reduction and related techniques\\
– Subsampling/partitioning approaches for ultra-high sample sizes\\
– Under- and oversampling approaches for data rebalancing.\\