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Percorso della pagina
  1. Economics
  2. Master Degree
  3. Scienze Statistiche ed Economiche [F8206B - F8204B]
  4. Courses
  5. A.A. 2025-2026
  6. 2nd year
  1. Data Science Methods M
  2. Summary
Insegnamento con unità didattiche Course full name
Data Science Methods M
Course ID number
2526-2-F8204B044
Course summary SYLLABUS

Blocks

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Teaching units

Course full name Machine Learning Course ID number 2526-2-F8204B044-F8204B044-1
Course summary
Course full name Course ID number 2526-2-F8204B044-F8204B044-2
Course summary

Course Syllabus

  • Italiano ‎(it)‎
  • English ‎(en)‎
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Obiettivi formativi

Metodi per la Data Science M è composto da due moduli:

Machine Learning M
Il corso si pone come obiettivo l'apprendimento delle tecniche di Machine Learning (ML) più efficaci, comprendendo i fondamenti teorici di ogni tecnica e acquisendo il know-how per poterle applicare con successo alla risoluzione di problemi pratici.
Per maggiorni informazioni: https://elearning.unimib.it/course/view.php?id=61220

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/view.php?id=61214

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

Contenuti sintetici

Data Mining M
Concetti basi del Machine Learning: tipologie di dati, istanze, features, tasks e scenarios, parametri e iper-parametri, misure di performance.
Tecniche di apprendimento non-supervisionato.
Tecniche di apprendimento supervisionato: classificazione e regressione.
Modellare non-linearità nei dati: tecniche basate sul concetto di kernel.
Automated Machine Learning: configurazione automatica di un modello di Machine Learning.
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
Metodi basati sugli alberi e aspetti computazioni.
Deep Learning per dati non strutturati.
Stima dell'incertezza.
https://elearning.unimib.it/course/view.php?id=61214

Programma esteso

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Prerequisiti

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Metodi didattici

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Modalità di verifica dell'apprendimento

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Testi di riferimento

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Periodo di erogazione dell'insegnamento

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Lingua di insegnamento

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Sustainable Development Goals

ISTRUZIONE DI QUALITÁ
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Learning objectives

Data Science methods M is composed by two modules

Machine Learning M
The course aims at learning the most effective Machine Learning (ML) techniques, understanding the theoretical foundations of each technique and acquiring the know-how to successfully apply them to solving practical problems.
For more details, please see: https://elearning.unimib.it/course/view.php?id=61220

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/view.php?id=61214

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

Contents

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Detailed program

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Prerequisites

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Teaching methods

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Assessment methods

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Textbooks and Reading Materials

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Semester

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

Teaching language

Machine Learning M
https://elearning.unimib.it/course/view.php?id=61220

Statistical Learning M
https://elearning.unimib.it/course/view.php?id=61214

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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