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Percorso della pagina
  1. Economics
  2. Master Degree
  3. Scienze Statistiche ed Economiche [F8206B - F8204B]
  4. Courses
  5. A.A. 2026-2027
  6. 2nd year
  1. Data Science Methods M
  2. Summary
Insegnamento con unità didattiche Course full name
Data Science Methods M
Course ID number
2627-2-F8206B022
Course summary SYLLABUS

Blocks

Skip Teaching units

Teaching units

Course full name Machine Learning Course ID number 2627-2-F8206B022-F8206B022-1
Course summary
Course full name Statistical Learning Course ID number 2627-2-F8206B022-F8206B022-2
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=68798

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/enrol/index.php?id=68781

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

Contenuti sintetici

Machine Learning 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.
Il paradigma connessionista: le reti neurali e i modelli generativi
Automated Machine Learning: configurazione automatica di un modello di Machine Learning.
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
Metodi basati sugli alberi e aspetti computazioni.
Deep Learning per dati non strutturati.
Stima dell'incertezza.
https://elearning.unimib.it/enrol/index.php?id=68781

Programma esteso

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Prerequisiti

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Metodi didattici

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Modalità di verifica dell'apprendimento

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Testi di riferimento

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Periodo di erogazione dell'insegnamento

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Lingua di insegnamento

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

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=68798

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/enrol/index.php?id=68781

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=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Detailed program

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Prerequisites

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Teaching methods

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Assessment methods

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Textbooks and Reading Materials

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Semester

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Teaching language

Machine Learning M
https://elearning.unimib.it/course/view.php?id=68798

Statistical Learning M
https://elearning.unimib.it/enrol/index.php?id=68781

Sustainable Development Goals

QUALITY EDUCATION
Enter

Key information

ECTS
12
Term
Second semester
Activity type
Mandatory to be chosen
Course Length (Hours)
89
Degree Course Type
2-year Master Degree
Language
Italian

Students' opinion

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Bibliography

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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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