DATA ANALYSIS AND STATISTICAL LEARNING

12 CFU - 1° e 2° semestre

Docenti titolari dell'insegnamento

ANTONIO PUNZO - Modulo DATA ANALYSIS - SECS-S/01 - 6 CFU
SALVATORE INGRASSIA - Modulo STATISTICAL LEARNING - SECS-S/01 - 6 CFU


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Contenuti del corso



Testi di riferimento


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Programmazione del corso

DATA ANALYSIS
 ArgomentiRiferimenti testi
1Statistical Models for Univariate Random VariablesSlides 
2Basics of MatricesBishop 2007, Appendix C 
3Basics of Multivariate ModellingMcNeil, Frey and Embrechts 2005, Chapter 3 
4Principal Component Analysis (PCA)James, Witten, Hastie, Tibshirani 2017, Chapter 10 
5Cluster Analysis (CA)Kassambara 2017, Chapter 3 
6Hierarchical clustering methodsKassambara 2017, Chapter 7 
7Partitioning (or partitional) clustering methodsKassambara 2017, Chapters 4–5 
8Cluster ValidationKassambara 2017, Chapters 11–14 
9Model-Based ClusteringKassambara 2017, Chapter 18 
STATISTICAL LEARNING
 ArgomentiRiferimenti testi
1Fundamentals of Statistical Learning: Estimation of dependencies based on empirical data; supervised and unsupervised learning; regression and classification problemsTextbook #1: Chap 1 and Chap 2, Sect. 2.1 
2Fundamentals of Statistical Learning: Parametric and non-parametric models; assessing model accuracy; Lab: introduction to RTextbook #1: Chap 2, Sect. 2.2 and 2.3 
3Linear Regression: simple linear regression and multiple linear regression; least squares criterion and parameter estimationTextbook #1: Chap 3, Sect. 3.1 and 3.2 
4Linear Regression: assessment of model fit; qualitative predictors; extension of the linear model and non-linear relationship; Lab with RTextbook #1: Chap 2, Sect. 3.3 and 3.6 
5Classification: logistic regression; linear and quadratic discriminant analysis; Lab with RTextbook #1: Chap. 4 
6Linear model selection and regularization. Variable selection; dimension reduction methods; Lab with RTextbook #1: Chap. 6 
7Support Vector Machines: support vector classifiers; lab with RTextbook #1: Chap. 6. 
8Neural networks: deep learning and multilayer perceptrons; Lab with RCourse notes 
9Mixture models: mixtures of distributions; mixtures of regressions; Lab with RCourse notes 


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