Fabio POSTIGLIONE | STATISTICAL DATA ANALYSIS

cod. 0622700059

STATISTICAL DATA ANALYSIS

	0622700059
	DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA
	EQF7
	COMPUTER ENGINEERING
	2019/2020

CURRICULUM ARTIFICIAL INTELLIGENCE

	OBBLIGATORIO
	YEAR OF COURSE 1
	YEAR OF DIDACTIC SYSTEM 2017
	SECONDO SEMESTRE

TEACHING UNITS

	SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
1	STASTICAL DATA ANALYSIS
	SECS-S/02	3	24	LESSONS	SUPPLEMENTARY COMPULSORY SUBJECTS
	SECS-S/02	3	24	EXERCISES	SUPPLEMENTARY COMPULSORY SUBJECTS
2	STASTICAL DATA ANALYSIS
	ING-INF/03	1	8	EXERCISES	SUPPLEMENTARY COMPULSORY SUBJECTS
	ING-INF/03	2	16	LESSONS	SUPPLEMENTARY COMPULSORY SUBJECTS

PROFESSORS

	FABIO POSTIGLIONE1 T Curriculum
	VINCENZO MATTA2 Curriculum

	Objectives
	THE COURSE HAS THE TWOFOLD PURPOSE OF: I) ILLUSTRATING THE MAIN METHODOLOGIES OF INTEREST FOR STATISTICAL DATA ANALYSIS; II) APPLYING SUCH METHODOLOGIES TO RELEVANT PRACTICAL PROBLEMS, USING TOOLS COMMONLY EMPLOYED FOR STATISTICAL ANALYSIS, DATA VISUALIZATION AND PROCESSING. KNOWLEDGE AND UNDERSTANDING. •ACQUISITION OF THE MAIN STATISTICAL INFERENCE AND DATA ANALYSIS. •PARAMETRIC VS. NON PARAMETRIC APPROACHES. SUPERVISED VS. UNSUPERVISED APPROACHES. •ACQUISITION OF THE MAIN TECHNIQUES AND TOOLS FOR BIG DATA ANALYSIS. APPLICATION KNOWLEDGE AND UNDERSTANDING. •ABILITY TO APPLY THE MAIN TECHNIQUES FOR STATISTICAL INFERENCE AND DATA ANALYSIS TO PRACTICAL PROBLEMS (E.G., SOCIAL OR BIOMEDICAL DATA). •ABILITY TO EXAMINE BIG DATA, ARRANGED IN RATHER COMPLEX AND/OR HETEROGENEOUS STRUCTURES • ABILITY TO USE SOFTWARE (E.G., R, MATLAB) FOR STATISTICAL DATA ANALYSIS, DATA VISUALIZATION AND PROCESSING. •ABILITY TO USE TOOLS OF PRACTICAL INTEREST FOR DATA ANALYTICS (E.G., APACHE SPARKS) .

THE COURSE HAS THE TWOFOLD PURPOSE OF: I) ILLUSTRATING THE MAIN METHODOLOGIES OF INTEREST FOR STATISTICAL DATA ANALYSIS; II) APPLYING SUCH METHODOLOGIES TO RELEVANT PRACTICAL PROBLEMS, USING TOOLS COMMONLY EMPLOYED FOR STATISTICAL ANALYSIS, DATA VISUALIZATION AND PROCESSING.

KNOWLEDGE AND UNDERSTANDING.
•ACQUISITION OF THE MAIN STATISTICAL INFERENCE AND DATA ANALYSIS.
•PARAMETRIC VS. NON PARAMETRIC APPROACHES. SUPERVISED VS. UNSUPERVISED APPROACHES.
•ACQUISITION OF THE MAIN TECHNIQUES AND TOOLS FOR BIG DATA ANALYSIS.

APPLICATION KNOWLEDGE AND UNDERSTANDING.
•ABILITY TO APPLY THE MAIN TECHNIQUES FOR STATISTICAL INFERENCE AND DATA ANALYSIS TO PRACTICAL PROBLEMS (E.G., SOCIAL OR BIOMEDICAL DATA).
•ABILITY TO EXAMINE BIG DATA, ARRANGED IN RATHER COMPLEX AND/OR HETEROGENEOUS STRUCTURES
• ABILITY TO USE SOFTWARE (E.G., R, MATLAB) FOR STATISTICAL DATA ANALYSIS, DATA VISUALIZATION AND PROCESSING.
•ABILITY TO USE TOOLS OF PRACTICAL INTEREST FOR DATA ANALYTICS (E.G., APACHE SPARKS) .

	Prerequisites
	PREREQUISITES: SUITABLE KNOWLEDGE OF MATHEMATICS AND FUNDAMENTALS OF PROBABILITY AND STATISTICS.

	Contents
	- FUNDAMENTALS OF STATISTICS (HOURS FOR LECTURE/EXERCISES: 7/3) STATISTICAL INFERENCE, PARAMETRIC METHODS, MAXIMUM LIKELIHOOD. DECISION THEORY. BAYESIAN APPROACH. - DATA NORMALIZATION. WHITENING (1/1) - INTRODUCTION TO SUPERVISED LEARNING AND LINEAR MODELS (6/3) MULTIPLE LINEAR REGRESSION. GENERALIZED LINEAR MODELS. CLASSIFICATION (11/5) LOGISTIC REGRESSION. LINEAR DISCRIMINANT ANALYSIS. BAYESIAN FORMULATION OF REGRESSION/CLASSIFICATION. BIAS AND VARIANCE. NAÏVE-BAYES. NONPARAMETRIC SUPERVISED APPROACHES. EXAMPLES: NAÏVE-KERNEL, NEAREST-NEIGHBOR AND K-NEAREST-NEIGHBOR. - RESAMPLING (2/1) CROSS-VALIDATION (LOO, K-FOLD). BOOTSTRAP. - LINEAR MODEL SELECTION AND REGULARIZATION (9/3) STEPWISE SELECTION. RIDGE REGRESSION. LASSO. DIMENSIONALITY REDUCTION. PRINCIPAL COMPONENT REGRESSION. EXTENSION TO HIGH-DIMENSIONAL DATA. SPARSITY-AWARE METHODS FOR BIG DATA ANALYTICS. - GENERALIZED ADDITIVE MODELS AND TREE-BASED METHODS (HOURS: LESSONS/EXERCISES/LABORATORY 1/0/0) - SUPPORT VECTOR MACHINES (1/1) - UNSUPERVISED LEARNING (11/5) PRINCIPAL COMPONENTS ANALYSIS. CENTROID-BASED CLUSTERING: K-MEANS. HIERARCHICAL CLUSTERING. OTHER EXAMPLES OF CLUSTERING. GAUSSIAN MIXTURES AND THE EXPECTATION-MAXIMIZATION ALGORITHM. DENSITY-BASED CLUSTERING: DBSCAN. NONPARAMETRIC STATISTICS AND - INTRODUCTION TO FUNCTIONAL DATA ANALYSIS (2/0) - SOFTWARE AND TOOLS: R MATLAB APACHE SPARK

Contents

- FUNDAMENTALS OF STATISTICS (HOURS FOR LECTURE/EXERCISES: 7/3)
STATISTICAL INFERENCE, PARAMETRIC METHODS, MAXIMUM LIKELIHOOD. DECISION THEORY. BAYESIAN APPROACH.

- DATA NORMALIZATION. WHITENING (1/1)

- INTRODUCTION TO SUPERVISED LEARNING AND LINEAR MODELS (6/3)
MULTIPLE LINEAR REGRESSION. GENERALIZED LINEAR MODELS.

CLASSIFICATION (11/5)
LOGISTIC REGRESSION. LINEAR DISCRIMINANT ANALYSIS. BAYESIAN FORMULATION OF REGRESSION/CLASSIFICATION. BIAS AND VARIANCE. NAÏVE-BAYES. NONPARAMETRIC SUPERVISED APPROACHES. EXAMPLES: NAÏVE-KERNEL, NEAREST-NEIGHBOR AND K-NEAREST-NEIGHBOR.

- RESAMPLING (2/1)
CROSS-VALIDATION (LOO, K-FOLD). BOOTSTRAP.

- LINEAR MODEL SELECTION AND REGULARIZATION (9/3)
STEPWISE SELECTION. RIDGE REGRESSION. LASSO. DIMENSIONALITY REDUCTION. PRINCIPAL COMPONENT REGRESSION. EXTENSION TO HIGH-DIMENSIONAL DATA. SPARSITY-AWARE METHODS FOR BIG DATA ANALYTICS.

- GENERALIZED ADDITIVE MODELS AND TREE-BASED METHODS (HOURS: LESSONS/EXERCISES/LABORATORY 1/0/0)

- SUPPORT VECTOR MACHINES (1/1)

- UNSUPERVISED LEARNING (11/5)
PRINCIPAL COMPONENTS ANALYSIS. CENTROID-BASED CLUSTERING: K-MEANS. HIERARCHICAL CLUSTERING. OTHER EXAMPLES OF CLUSTERING. GAUSSIAN MIXTURES AND THE EXPECTATION-MAXIMIZATION ALGORITHM. DENSITY-BASED CLUSTERING: DBSCAN. NONPARAMETRIC STATISTICS AND

- INTRODUCTION TO FUNCTIONAL DATA ANALYSIS (2/0)

- SOFTWARE AND TOOLS:
R
MATLAB
APACHE SPARK

	Teaching Methods
	THE COURSE INCLUDES THEORETICAL LECTURES AND CLASSROOM EXERCISES ALSO WITH THE USAGE OF COMPUTERS.

	Verification of learning
	THE FINAL EXAM CONSISTS OF DISCUSSING A PROJECT WORK, AIMED AT EVALUATING: THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED DURING THE COURSE; THE ABILITY OF SOLVING STATISTICAL-DATA-ANALYSIS PROBLEMS APPLYING THE METHODS AND TOOLS ILLUSTRATED DURING THE COURSE. FURTHERMORE, THE PERSONAL JUDGEMENT, THE COMMUNICATION SKILLS AND THE LEARNING ABILITIES ARE ALSO EVALUATED.

	Texts
	AN INTRODUCTION TO STATISTICAL LEARNING, G. JAMES, D. WITTEN, T. HASTIE, R. TIBSHIRANI, SPRINGER, 2013. AN ELEMENTARY INTRODUCTION TO STATISTICAL LEARNING, S. KULKARNI, G. HARMAN, WILEY, 2010.

	More Information
	THE COURSE LANGUAGE IS ENGLISH.

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Fabio POSTIGLIONE | STATISTICAL DATA ANALYSIS