Statistical learning (2020/2021)

Course code
4S008279
Credits
6
Coordinator
Paolo Dai Pra
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.

Syllabus

The entire course will be available online. In addition, a number of the lessons/all the lessons (see the course
schedule) will be held in-class.

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.

Assessment methods and criteria

See the assessment methods for Part I and Part II.

The assessment methods could change according to the academic rules

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