Master's degree in Mathematics

Statistical learning (2019/2020)

Course code
4S008279
Credits
6
Coordinator
Alberto Castellini

Teaching is organised as follows:
Unit Credits Academic sector Period Academic staff
PART I 3 MAT/06-PROBABILITY AND STATISTICS I semestre Paolo Dai Pra
PART II 3 INF/01-INFORMATICS I semestre Alberto Castellini

Learning outcomes

The objective is to introduce students to statistical modelling and exploratory data analysis. The mathematical foundations of Statistical Learning (supervised and unsupervised learning, deep learning) are developed with emphasis on the underlying abstract mathematical framework, aiming to provide a rigorous, self-contained derivation and theoretical analysis of the main models currently used in applications. Complimentary laboratory sessions will illustrate the use of both the key algorithms and relevant case studies, mainly by using standard software environments such as R or Python.

Reference books
Author Title Publisher Year ISBN Note
T. Hastie, R. Tibshirani, J. Friedman. The elements of statistical learning. Data mining, inference, and prediction. (Edizione 2) Springer 2009




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