- Economics
- Master Degree
- Scienze Statistiche ed Economiche [F8206B - F8204B]
- Courses
- A.A. 2025-2026
- 2nd year
- Data Science Methods M
- Summary
Course Syllabus
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
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