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Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2017/2018
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 097382 - APPLIED STATISTICS
Docente Secchi Piercesare
Cfu 5.00 Tipo insegnamento Monodisciplinare

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Des (1 liv.)(ord. 270) - BV (1153) DESIGN DEGLI INTERNI*AZZZZ098593 - APPLIED STATISTICS
Des (1 liv.)(ord. 270) - BV (1154) DESIGN DELLA COMUNICAZIONE*AZZZZ098593 - APPLIED STATISTICS
Des (1 liv.)(ord. 270) - BV (1155) DESIGN DELLA MODA*AZZZZ098593 - APPLIED STATISTICS
Des (1 liv.)(ord. 270) - BV (1156) DESIGN DEL PRODOTTO INDUSTRIALE*AZZZZ098593 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1092) DESIGN DEGLI INTERNI - INTERIOR DESIGN*AZZZZ050817 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1097) DESIGN FOR THE FASHION SYSTEM - DESIGN PER IL SISTEMA MODA*AZZZZ050817 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1159) PRODUCT SERVICE SYSTEM DESIGN - DESIGN PER IL SISTEMA PRODOTTO SERVIZIO*AZZZZ050817 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1160) DESIGN DEL PRODOTTO PER L'INNOVAZIONE*AZZZZ050817 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1162) DESIGN DELLA COMUNICAZIONE*AZZZZ050817 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1261) INTEGRATED PRODUCT DESIGN*AZZZZ050817 - APPLIED STATISTICS
Ing Ind - Inf (Mag.)(ord. 270) - BV (479) MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE*AZZZZ099433 - APPLIED STATISTICS FOR ENG4SD
097382 - APPLIED STATISTICS

Programma dettagliato e risultati di apprendimento attesi

 

The course covers new approaches in the areas of statistical modeling and data analysis, using ideas that bridge the gap between statistics and computer science and developing tools for the statistical mining of big data. The focus is on predictive learning, with particular emphasis on recent advances in regression and classification.

 

Program:

1) Introduction to statistical learning.

2) Dimension reduction. Principal Component Analysis.

3) Linear Models. Simple and multiple linear regression. Estimating the coefficients, assessing the accuracy of the coefficient estimates, assessing the accuracy of the model. Qualitative predictors. Model selection and regularization: subset selection, shrinkage methods (ridge regression and lasso), dimension reduction methods.

4) Supervised classification. Logistic regression. Linear and Quadratic discriminant analysis.

5) Unsupervised classification. Hierarchical clustering, K-means clustering.

6) Resampling methods. Cross-validation. The bootstrap.

7) Tree-based methods. Classification and regression trees. Bagging, random forests, boosting.

 

Following a blended learning approach, the course will make extensive use of the Statistical Learning MOOC by Hastie and Tibshirani referenced in the Bibliography. All methods will be illustrated using applications from marketing, finance, biology and other areas; the R free software environment for statistical computing and graphics (downloadable at www.r-project.org ) will be used and illustrated throughout the course and its lab sessions.

Through the course, students are expected to work in team on a real data analysis project whose progress will be shown periodically to the class.

 

 

 


Note Sulla Modalità di valutazione

The exam consists of two parts: a written exam and the presentation of the data analysis team project. To obtain a positive final mark for the course the student should pass each part of the exam with a grade greater than or equal to 18/30; the final mark is then obtained as the weighted average of two grades, with weights respectively equal to 0.6 for the written exam and 0.4 for the project presentation


Bibliografia
Risorsa bibliografica obbligatoriaJames G., Witten D., Hastie T., Tibshirani R., An introduction to statistical learning, with application to R, Editore: Springer, Anno edizione: 2013 http://www-bcf.usc.edu/~gareth/ISL/getbook.html
Risorsa bibliografica obbligatoriaStatistical Learning MOOC by Hastie and Tibshirani http://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/
Risorsa bibliografica facoltativaJohnson R.A., Wichern, D.W., Applied Multivariate Statistical Analysis , Editore: Prentice Hall, Anno edizione: 2002

Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
32.0
esercitazione
16.0
laboratorio informatico
0.0
laboratorio sperimentale
0.0
progetto
0.0
laboratorio di progetto
0.0

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.1 / 1.6.1
Area Servizi ICT
28/02/2020