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Percorso della pagina
  1. Economics
  2. Master Degree
  3. Scienze Statistiche ed Economiche [F8206B - F8204B]
  4. Courses
  5. A.A. 2024-2025
  6. 2nd year
  1. Data Science M
  2. Summary
Insegnamento con unità didattiche Course full name
Data Science M
Course ID number
2425-2-F8204B018
Course summary SYLLABUS

Blocks

Skip Teaching units

Teaching units

Course full name Statistical Learning Course ID number 2425-2-F8204B018-F8204B033M
Course summary SYLLABUS
Course full name Data Mining Course ID number 2425-2-F8204B018-F8204B034M
Course summary SYLLABUS

Course Syllabus

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  • English ‎(en)‎
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Obiettivi formativi

Data Science M è composto da due moduli:

Data Mining M
Il corso si pone come obiettivo l'approfondimento di tecniche per l'analisi dei dati e di data mining e il perfezionamento delle abilità di modellizzazione con finalità previsiva, con relative implementazioni nell’ambiente di programmazione R.
Per maggiorni informazioni: https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
Il corso si pone come obiettivo l'acquisizione delle principali tecniche di statistical learning (SL) e la loro implementazione nell’ambiente di programmazione R. Durante il corso verrà data particolare enfasi alla algorithmic modeling culture, prestando anche attenzione alla stima dell'incertezza nelle previsioni.
Per maggiori informazioni : https://elearning.unimib.it/course/info.php?id=55091

Entrambi i corsi contribuiscono al raggiungimento degli obiettivi formativi nell’area di apprendimento del CdS: “Statistica”.

Contenuti sintetici

Data Mining M
A-B-C: modelli lineari ed aspetti computazionali
Compromesso distorsione e varianza, ottimismo
Selezione del modello e metodi penalizzati per modelli lineari (regressione ridge, lasso, elastic-net)
Regressione nonparametrica (regressione lineare locale, splines di regressione e di lisciamento)
Modelli additivi (GAM and MARS)
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
Metodi basati sugli alberi e aspetti computazioni.
Deep Learning per dati non strutturati.
Stima dell'incertezza.
https://elearning.unimib.it/course/info.php?id=55091

Programma esteso

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Prerequisiti

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Metodi didattici

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Modalità di verifica dell'apprendimento

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Testi di riferimento

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Periodo di erogazione dell'insegnamento

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Lingua di insegnamento

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Sustainable Development Goals

ISTRUZIONE DI QUALITÁ
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Learning objectives

Data Science M is composed by two modules

Data Mining M
The course aims to provide data analysis and data mining tecniques and to improve predictive modelling skills by using the R software environment for statistical computing.
For more details, please see: https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
The course aims to acquire the main techniques of statistical learning (SL) and their implementation in the R programming environment. During the course, emphasis will be placed on the algorithmic modelling culture, while also paying attention to the estimation of uncertainty in predictions.
For more details, please see : https://elearning.unimib.it/course/info.php?id=55091

Both courses contribute to the achievement of the learning objectives in the subject area of the MSc: "Statistics".

Contents

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Detailed program

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Prerequisites

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Teaching methods

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Assessment methods

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Textbooks and Reading Materials

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Semester

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Teaching language

Data Mining M
https://elearning.unimib.it/course/info.php?id=55081

Statistical Learning M
https://elearning.unimib.it/course/info.php?id=55091

Sustainable Development Goals

QUALITY EDUCATION
Enter

Key information

ECTS
12
Term
First semester
Activity type
Mandatory to be chosen
Course Length (Hours)
84
Degree Course Type
2-year Master Degreee
Language
Italian

Students' opinion

View previous A.Y. opinion

Bibliography

Find the books for this course in the Library

Enrolment methods

Manual enrolments
Guest access

Sustainable Development Goals

QUALITY EDUCATION - Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
QUALITY EDUCATION

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