Servizi per la didattica
PORTALE DELLA DIDATTICA

Statistical learning

03REURT

A.A. 2018/19

Course Language

Italian

Course degree

Doctorate Research in Matematica Pura E Applicata - Torino

Course structure
Teaching Hours
Lezioni 15
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Fontana Roberto Professore Associato SECS-S/01 5 0 0 0 5
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
*** N/A ***    
2018/19
FinalitÓ del corso: in italiano e in inglese Il corso, che ha come prerequisito la conoscenza dei fondamenti della teoria delle probabilitÓ e della statistica inferenziale, completa la formazione dello studente di dottorato su: 1. modelli gerarchici e statistica bayesiana; 2. metodi statistici di analisi multivariata;; 3. metodi statistici per la pianificazione degli esperimenti. I metodi verranno illustrati in concreto mediante applicazioni del software R e/o SAS a problemi di tipo industriale, scientifico e gestionale, in modo da rendere il corso di interesse per un ampio spettro di studenti di dottorato. Knowledge of
Knowledge of the basics of probability theory and inferential statistics is a prerequisite. The course aims at completing the education of Ph.D. students about: 1. Bayesian Networks and Bayesian Statistics; 2. methods for multivariate statistical analysis; 3. statistical methods for the Design of Experiments (DOE). All methods will be illustrated in practice using the R or the SAS software on applications to industrial, scientific and management problems, in order to make the course useful and appealing to a broad audience of Ph.D. students.
Bayesian Networks and Bayesian Statistics:  naive Bayesian networks;  fundamentals of Bayesian Statistics;  industrial and scientific applications. Multivariate analysis methods:  principal component analysis;  cluster analysis;  applications to industrial and management problems. Design of Experiments:  orthogonal fractional factorial
Bayesian Networks and Bayesian Statistics:  naive Bayesian networks;  fundamentals of Bayesian Statistics;  industrial and scientific applications. Multivariate analysis methods:  principal component analysis;  cluster analysis;  applications to industrial and management problems. Design of Experiments:  orthogonal fractional factorial Bayesian Networks and Bayesian Statistics:  naive Bayesian networks;  fundamentals of Bayesian Statistics;  industrial and scientific applications. Multivariate analysis methods:  principal component analysis;  cluster analysis;  applications to industrial and management problems. Design of Experiments:  orthogonal fractional factorial
ModalitÓ di esame:
Exam:
Esporta Word


© Politecnico di Torino
Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY
Contatti